Descriptors comparison (oldschool vs newschool)

rodrigo.constanzo · May 26, 2019, 12:04am

After asking @tremblap and @weefuzzy a bunch of questions at the 2nd plenary, I sat down to create a patch to extract the main onset descriptors I want from incoming audio in a JIT manner.

It was a bit confusing at first as it takes lots more steps to get to the same kind of information but I got there in the end, and the results are pretty interesting.

At the moment I haven’t started to compare the actual analysis data yet (I don’t really understand what “the spectral centroid (1) in spectral bins as units” means from the fluid.bufspectralshape~ helpfile (what would it be for fft settings of 256 64? would it be 256 and I would divide the output of the centroid by 256 to get a normalized value? (why wouldn’t it be normalized?))) but I did find out some interesting results in terms of how fast/expensive the process is.

The first is in terms of what it takes to do the same job:

I do appreciate the fact that you have tons more control in the “newschool” way of doing things. using diff fft settings etc… but it takes 9 core objects and corresponding buffers to do the same thing that a single descriptors~ object did.

Not great to read/code, but not the end of the world, especially if it’s better.

The biggest difference (so far) is the processing speed.

You can test the patch below to compare for yourself (save first so all the loadbangs fire), but on my computer the descriptors~ approach takes an average of 0.05ms to complete a single block of analysis. The FluCoMa equivalent takes 1.0ms to complete… so about 20 times slower, and more importantly adding an appreciable amount of milliseconds (1ms) to each cycle of computation. That’s pretty big when you’re dealing with only 512 samples anyways (almost a 10% increase in latency).

I don’t know if this is because it is computing lots of things I am not using (each fluid.descriptors~ object computes every possible descriptor, and each fluid.bufstats~ object computes every possible statistic) or if it is because there’s multiple layers of reading/writing buffers involved, including some Max-rate peek~-ing at the end, or if perhaps the algorithms aren’t optimized yet, but the performance difference is pretty night and day.

Thoughts?


----------begin_max5_patcher----------
11121.3oc68s9iaijjme18eEDENrn66JqIe+31cV3a58t99P2XNf9CCFzyBC
VknphikD0RQY2dZL0e6W9fjhuYRIRVztUA3pjIEkx7WDYjQDY7329l2b2CQ+
Zvw679e58Kdu4M+127l2Xtj9BuI8++l614+qOt0+n4sc29fOE8ve+t6s2JI3
WSLW9f25fiO9qwgGRhhOlc68m1EcJYaPh4YgoWMbs4QTeLukPxdq12WxmODX
GM2cm2+Y5sdLZaTr8pfUT.TRYbFPHvXL7d0kjpeXHIhJg.IUeE.FBvRHEgjD
rjeuGbEH+i6fexiOGt+o2GG7Xh8iEQnq.26QwH8eDX8uQnBOiZhDtu57v7AE
DmBVon0ataS31fOFDeLLZeg28aty+vgBW9MEdDMD+2sSQw84WJbu8Rf7KEG7
wvrmmleU+X0zIQMWNEavt69UVFpp+XhVGDu+TnYnXu3+7axFRFx5d+cAGO3+
n8g0T+raeFfnRMj.AD8eXDf9ODA3LBoXadZazieHXcwg7cQGB1Gt+Pbvwf8I
9Ioi87auNXi+osIueSz9jig+CyH.poAMb+MoivFuodNXF9+uhC82lOAdJNbc
zd8fnDkPe4rutewCZn85eedxXdG68OzvCqXaUvRK27nZRd53C9wZB0CaMeCn
ralDEss7sxetsAaRRu8gv86qfhIQGZ+lwgO8bGO6CQpatqqOaycN99S6s288
Jdhj2ez+ikQ6D+saSW6W9i+W82GtyOIHIzRBPf7aFr2WMQe93iwQa2VZ9Zuy
Ga3NqU73OF7ov0IOa9hJxLnd6J4KoLQ2kSkWG9TvwjxWKw+oikuxwjOaA8BW
5zCoqgeeRvtCaUyhxugRRGKtfsnTxRWuKokUkXtQQXJdqFkVVUhIjV7QZWpY
ah5f.5JkLRLDuBBUhLIL.gHQPgPshlTUvWqB+ZR.XYfnUAgsILrxi2jPwVDL
1tvw9EP1lPxRBJcPXYEAlDjvfkDyNJDl4+gArxPayRMcPxoKRO6UBpiRQ6PR
paRS6QhZuRU6UxZORW6WBauRYcPRqKRaGhD2Nj51qj2tk91sD3tkB2oj31jF
2rD4VjJ6jj4lkNWUBRUoz0teeRqKKw1OIZ8CUuaqBsqJ3lV8I6V1cqpphjq3
TfVC2zezp2hwqnbljQytnRwXBrIo4MHQu389mey2T7+b+HgrOFsamRNVqPaR
jmRn5G9COpYF6DgAsivPvc8iebod6ODloAGtEh.eI.Qahi14ksx6rMVCGlDN
iRDCHI4e4.ReezdkZEIdGBUSIO+8q8dT81iiBW6sIN3+5Tv9G+rW3dE61tv0
g12v1vCdpKb5nVfnWr+9mBVU8KXa39fGiNs27sfuTjm4.xirVeZW7prf07e3
KAruGoi+8nv8djKV7HZbDOhsFpJPVd1FjU1u.QxqOXdZuKvIsCYg8Cm2W4et
CuRqCR33UX0ObrL8GzRY6ldP2ZldMLVUnfLNLqPFak5eLAKCAY58x4JKyvBI
L6h31rLqSqyZyBs5XWmVp0k0ZM7Q0lUacX4V2Vu4lEbcYEWMK4bzZttbAFQX
IH7F1Urci5bzvNWMtyIC7FfQd8Xnm6F64fAeNYzmSF94fwetY.nSFA5ngftZ
L3PMHrGiBcxvv9MNreCD62HwdMTrKiEa2fwNLZzYCGa23wlDn0zdOM99bRU0
lUW8rtom1GlTPA08QIAlqcum+QuOErcq9uZUVUbJO4ElbzSs0ygSIMq2Zu5t
1u9q8oCa65wZjPZsyBR4soGaOJHTSIg5JJL2DsGsDsf04lObP8ZM4pWzGcsn
OwQz2ZDATxVIoTNHWCMk9uBy9XX7WCjhp1DedgzzSJPChTXWGHEe8f8Y1MGG
3eLZ+YammdjGxccY.DQVgvDACmY.HQspf.VIjLFBkcQgVuNzWQjlzsUJtKxw
4ft.bktnP6ZzEgXk.yoTRlvJ58dLkA7LJDWz.okOMpQyOaX0CXkgY7tdAdXO
.Oi2zmQ+Fk1F8wFNDPBaEt5BElrMKQqSCvKaZf1I.diB7SmB3WRVgqtdfvcG
8eMWAXFEWOvJfSAvZ2KFCx9c+PYSB9xDDOb8PB2uN3WesoPVP7tqVluvU8fR
konTAhTUkzgPJf8PJFl14ChTT2GG58OczHRChne+tQqNFcJ9wL96bgLdMCPJ
Nujv849R5WJrqf9IFEtrqe7yG53WyZsbF+ZYQCD+oNO9q7lp5tx6z9BZ86s9
K689IIwgObJwx50kecuJ+u7z1nG72l5MkbuF1gaZ9l1mjeg5me7H4mek5tTA
FSJJz6le9u4m+a94+le9u4m+a94+le9u4m+a94+le9u4m+a94+le9u4m+a94
+le9u4m+a94+le9u4m+a94+le9+8te9aZaF28ie+dweanFbbN+GrImS6Ry5b
SkN2Po5HsY4VSDJ23VEtmcSj6bE+P3AAfvN.vtRbPm.uxYobiB5KiosIfoBx
VWvhRHAr1DsMQh35hTblpeMiPjyiPxqzHD33HD8JM9vNN9nuFiuL9JmQP3rO
BICbDhl4QHZ.iuWi0HCgBCeEFezARewcN9JbyhJS4hhT+10VQC5WwolTZ57D
H+Umg4Qr9xnSfzAWcYvnqq5xfvhUHfffyKDARssfHchhBQYlBJvY4Vd20ZFz
7fTu0CBWwfCFrPcWJd1rMxO4BQLhx5YkIyTp.SkXNQmZsVCqWDH1Q+cGNlDs
63KsCZnVpeQ7NAsigOsWGsBMBesGiD20YDOzHtCnMf6.iJoboC0Ao4AoO3+A
OeEh74iAdIFYjOo1SFrpr+LciekLjE2iIVqClFIsBVivUjJfzI+LEzOzSmGn
ufYYuL7xzEnSrtlAmy.dyI5jMGKxh2LhRlBmMub5UMj2QFWXmfY3dGDy1zxc
BncyPa0F9RleVbbU2byQ.vpYSd61iWBwvWBKBlxpyh3B7.esfmNV9p19X8C9
6eZ3bajN41LelS21RMoN.hIqKpjAl2kt6BNdzuzw9W3D11jbvO1e2QODk4wH
2W5PR1FcZ8d0Sq13R8IlVad.qXLucAqC826orh3840oGAvybNjpapt0wM6Je
khe+scr1NRmkuRaG1HMFC4ZZLhh.bfhTSzzXrs9+foCUmuyUxzZdz5LcuIK3
JP8qaadMS2Zyr2JdupStuAOJ.NNJfzIbTnYdbaT.lxQAwUJhbJGEnk.VfccT
Pp3UgQcTfbcMBFMkqQ3Uc1bWihxuwn301DI.75NzZ.IyGZvogOF5JeLuUZ2Y
AulfWtEIum2t3z5vne1D46u+mB1e57NGYwxeyi8GdZS31s4EF8lboU1VYokO
8RUx1hETcjj.gBcUSGCwb8tN.c7nfnzpmWg84fYOHgRj.j9sSXJiKLEdch.g
oXSIXugGEc96D.k1uIfTnCJL8qTWBVpTsaeTk9VoYC.uRcH8Pbzgn37TqXEV
V54NkD8Tr+5vTsQAM5Bt6KvtDqdmMEy42sSQHBK9YzD6VNQ8GO8nuqDxfsAZ
skKQQ3BFiafSA.JD1WQn.DpF5b2Q0GviZDn7m.BvjFZJjwQV5g..fj5e.Zc3
LO66C0ATwwfbZDVhfFZDJmPi3JsVpSiJyPQ3TH1LnQRrvVP9QTnZDU+AGBeb
NsrCV4AvVUlYVjyBK3.pPXeUiy1pLyLJvh0L8Ob6qreZyBubI3rDdHADHWZl
JRJgysLRHok4Roo9+YKpwpjy8mODr26m82ez6mC1E9Pz10kUH1+wGqx2h0LJ
FLmjAIZ4HjL59jtn6CG2Fp1c3unyeLWW7UXzaK2lE+0DOdUVBwJkHZcOTacY
J39B+p9xq1mhJFf9Hn1UA4xA3oKplGvohqhblRpD.o1CBIKx9okbQnv4ZnWy
T4og1NFhNKH.LS3U8W0i.PtPuq+8M9plezunzCn9J0gyQT0MdCfa1P+KnqkB
mvyhDUqu09SaOETrZx18HuocEfbFwtqX9jPpuDbFmDwQeZ+UMKNO1o4xTvy7
r36+r+0MI.HkZ.VQ4bLFYWiifPHd9lD+PbPvUNKLTAckGFa0vUq617MA9qAa
2F8I2mAE8b3uX7cHUB.hd26UoxL2JIEJLs4J0yQyUpbZmnm18PP7Onz46pHT
5kG.C6FUoBA0LafEktOoShXqBg5YwEI2kQXLrLcIBOU4YEAPh6QOqYYhm9Yj
8AzW7t7aWROTp63aoZrsX2L07aKc4J5lbIda8dn4lHGpglHGn8lH28UNvlt5
9aocCBaChn8t+1ELM2rMR8Yz8DZST7NeyHhUeJhy6SdlyYIHIH981D2uvg.d
gSeLQrxjBdljVs9Ave8S+VnxENNm9IxXZqDYajhbe4CBtqYJ0lx5xYal9O15
8TbzoCd4gEfCSXVmb0NMQgli2217oZehhdU3nEiDGcymLW6mJWG3UpD.hX1X
LN383gS5x2QrysLSrrmEBSBxX6LlLZObR+NryXhnE53HvzlkClOIsFSnRgBJ
7b6hPdqQYdqQY9UQix7BiyIXOgZA1rSOjaaXMzreO7HYRoZQP7G825Et2a2Q
uvidwAZeNEr1SgJAulA5zCdWPnKeMg3TaRgtvXUzHwja2yswVM1qRf2p1W9Q
sT6gGB8CMnvmbrMsV1QYKFv8sWT3Kub.1TwJDaI+AYLXDr.hK+KQT.cwHJvp
28WORBXKGFVBWyi9JIH3xB5a9.4KaW9WZwTwgc+aKLu0eadGUZWdzSo8lNGL
zEcBqF.G8xzMnQk.Pu5.o35AR6JyQCG80nVJP1I3AmqPaU3XjeMkgTJ2wwvT
F3fLWigxxoF7nNFbMBJYSYHs5Zj.NkAz5.nES0Xv0XpE2Q.QN8GTga9H4073
LBGvwYfws5fOsCtGvIZPDF41PDZIdjFvo6HML8C8E+QZfjW+QZjMS+h3HMPh
K+HMxlnK3iz.JWPGoQJd8EwQZffy4QZjgL2NRiaGowsiz31QZb6HMtcjF2NR
iaGowsiz31QZb6HMtcjF2NRiaGowWAGow0651uNbg8RvU9KgizXIbzNKgi35
5OpuaGowPyPCH85xPCaMHaBSPiQ4fafjq9farSzo7baJmaxEreAIVQgbDTpq
tjRwJDz6s5RzHAgNGx485JWX6txs6INipmqhzMj47w2+9s5u5+OaO88Q+juy
yQlCSwR4CFE.kTFmADBcYbyjAr5dbERhnRHvVhL.XH.KgTDRRvxxYccGqIR8
hcO3E72gdwtXe7EBssmOljLIdwFsBbyu027a8WE9stKmwbZylf3W7NdPwNE6
u83y9GBdulA53fKDzBWb908NZMmTeldBrjl0toHlxYps6nRYKlx4bF.ZpAlW
JvQGQfiKQsCb.9hC3xpknWJ1wlZrSWbOM5tIWbXW4UsJyT1MX7iIl7EsPotP
ZI.Rf.oKDpZm7QgKzUvWFHJm7EvMAh.3xc07EgibvjuXVo8XMbDQV1qrGLNR
.S8hZjfoKtwLn.xH.1REFMqoGL7gm7kyM.eb5hcw7vAPwTCfPNrJ.JmYcap2
OuaLpQcONQ3rdNfLp8HNTyZkLLNByvHgQBVZThtDNHxDOvviFAxU2MHzAFLB
H.nz9RNDaz5UVAnPnEyhrKAnfhqFnnJtjh.EvVU5Wv.UKMbcGQL3U1+qvvF1
8ivwUvKlCBevyWaByibA82F40gT1fxmhpxJQoh9wFxL07kBB9v.c1QKvkDOB
sJrlWLp1HCVBAgTz7xc08Va5N7wf2bSh5Kbkv7UXNUhAYsDEbSBlRi6cvhXW
d+eMb2ocCFKD7dvBNakDJPTAGKzyaDQuqOnpLG3hYW+MIQ6tjvkiSmBoy3pR
mIzEz19zgiR3qda+FApEt9QrgGlffqW+HaywZIiLstI1EvZQjiw1XBaSTu15
NHCsXTKxhZN4l91vJwHfUMuPTxphbfkFx4pe5ayFuwn0plt3rNZgnKFzZy1S
gqW8voMFb5Eu2YiWoF7Uu5VlUsMsR9cai9jG06cOG9zydRZq8HM7PcMCxgPo
9dWz1uIUWQJJCEqnK.LWYOAV2VJPXa9JJWBpozBs4rq+ynIsHm35vdnbbv9F
kgzD1CIKeruzIFjA+sKr4Jo.rQh62l.5MA41brbYA4JiXNDcLnEgQduqP7R1
lbp3jM5Lf1C38N0Tw75id7QjxflR4RXzJQYuEiHM3U4ENUy1mMKQspH4JiJg
JRkfvwiLwlRIX0oRPa6LdgSWxaGpkHM0ErM4qgFUgaMrlARlWpQWNeIu8x9G
ThrhC7Mu3CAexPH9Cde3TbRzwP8Kii1FsYi5Ea15mjd6Gi0Aq0v67rskhu.P
emcCVTWtDDW6TrR2+PYJ0B.iyYr+CdIwmBND3+gg63u9b1ECXR6KBoFT.wKG
e8Yk8pXbh1uIbcv9GCFNRP6yEnHwJENHDHHFHnRjf2LvjZbHXIzonKHW6+F7
duKpAciYiwQOnVDZiLXb+FChlakdKpU04cNRL6W7j261D3qCc5iU0LayljiJ
rP8dx5D2E26fVVqoqa2CLdB0.qgyq+K.Mvxj+0.A6Sg60Ausgn7tmiNX9OZp
SNk7rVAe3SA5viV+bps9yjjVVMfwkTNpZBzDsyFK9KKpkQDcSqsJrFRAykoR
o5T6u8on3vjm2UV04wkp.lPkmahHAvyKQ5xR8W7H4ydh52leLdEjH0YbiMkZ
vfKIOfm7xkxEVNY5SKBKZvf0BRHGvB30hESRhO6bW7dJy1VSjj31vPGIESVF
257v.Mkk1VnqIer9H4ltQgqo9qbJyIdsaccZTvmxQAx0TRuAPKJdsMEBgutC
MLo8gFXZpmAfEPh7igKghSrqk1A3TlK+XWKtCD4TNJnCRT+jMLbUHaCbwS7R
Z2GZv1GZnW4gFdtk1Pbc+J9Tt2MQNHta7TUBUbUzKlt.JhJvobgN24xpiXJG
ENK0aR2BPhGD6I5UtRGgmTiN.xQn31TJ4yawlry0tfSqCi9YSED38+Tv9St1
pke3oMga2lWkLdSs2watKyr9zZow8EtUopqARRfPgogrCwbjouKiUufRMlUU
64fYOHIumtSXHNwTENHBDlZ5q3fFdTz4uSc6b17MAjB.w9pBc34hOp+9mRqp
B7ykF.KANNRWu0xJwDqvxRO2ojnmh8WGlZWc9tLmoWSSqy9GO8nuqDxfsA6p
03uELF2.mB.THruhPAHT8Vm8Q0GviZDn7m.BvjBa6cmirzCA..I0+.zd1y7r
uObutXkDjSivRDzPiP4DZDWPv0oQkYnHbJDaFzHIVXqNKHJTMhZpye6NebNs
rCV4AvVUlYVjyBK3.pPXeUiy1pLyLJvh0L8Ob6qreZyBub6MRcIf.41dvtjR
R6A6DjzxbQY0J0jYrvJwb+4CA689Y+8G894fcgOnaC7kd6Mzv5wZFEClSxfD
sbDhbN5W8ev1u5+K5BiyEzv5MgtUoeMwi2c9+JqPk2ougZqKSA2W3W0Wd09T
Tw.zGA0tJHWN.OcQ07.NU7usyTRk.H0dPHYQ1OsjKBENWC8Zmf6zPaGCQmED
.lI7p9q5Q.HWn20+9FeUyO5WT5ATek5v4Hpd5CCfa1P+KnqkBmvyhDU6o.9m
1dJnXrN08HuocEfbFwtqX9jPpuDbFmDwQeZ+UMKNO1o4xTvy7r36+r+0MI.H
kZ.VQ4bLFYWiifPHd9lD+PbPvUNKLTAr9GqFtZc2luIveMX61nO49LnXgt6W
Lk5NpD.D8t2qRkYtURJTXp4gpmilqT4zNQOs6gf3ePoy2UQnzKO.F1MpREBp
Y1.KJceRmDwVEB0yhKRtKivXXY5RDdpxyJBfD2idVu1S7yya2odULafCAlAs
DSX.YpYCbX88yWdj8D+CCYkYAKd9Q+jHueTG6P2MFiw4nLGOCEv3xgoYgLF8
4yI1fWd3A+svuS2ObxuwCgJCH8O5cZeXxw+1d8CkGWveKy7dW+mx9raJpfaN
JMJWzIXczsx31pWnjZKYDLV038cBKIv55KpRnMG6wHqjRko3P0HPoPDSJMwS
pZI04B9qC0F3Kr3.aK+aSYsAVofsV2bmmIXzE0p+5saNRIqfHFR.SSkeSVwZ
CvdR1um7pdsRlSPrREAmgCJ9BK5y1HwBy0kCYkZobJVZCFMJYF6WgQa1bL21
l9mshKb1pS6zpDWLUmbpLrREIL0lRX80pFA+9qTNiPLayIxldOoHDAbqTNeq
TNeqTNegYhgUpmGCeA40zE0cn5MvbQo0pcaV7kUZMlq7xPoLaRz9qqEt0gx.
cDE281ymJ0khpoGvqWVN8+d+QSTwm7rehmVjzwmiRdwKw+ClKF3oWK6czWwa
Gn1k0boOplVQwdJKU7+nVg6rdlzwimBNt5BBedfCsCxlX1fhLMo083VPV2f5
0udsDGnDqutrVICn5+bk0kMnMM.z8dDtZCWHQvRyQ.kF3KmBizauf1Bn35q2
OonixDDAkiAbBlXqLvM0VZd0xiL+0VEA7NF9zGufhjd24CT01ReqZ2SOu.Sv
m2jr6BSVB7cts3fwWQ4PoZ8AGInPpdwwWtILgldWNRYZIpgDSYz4KbM.+3SZ
r0IGPDdCmynA04QFdtitWWCBaA7Uskp8J5qw2pytPSC1ba3wjf8Z0edJ1W6a
QkZQ95XnypDkebf2i9GzFSu9NWcfHgdWW9XAkZeod2Ids9j5j4WksQ9q01e3
kusqCtV4B8rBfloyhcy44y2QOGdvOujm2+DDwacFp1qdu+1Nmm19eWpCAo3d
bC5u+7SjjXMNhYXGnVOmioza9I5leht4mnKzHekfhj.01QoaSojaMdEBGYO5
aKscp.oNTU.RtRuaISncRNaAU1ar3R39wCV5qF3fRsBQuktsl8sLPB6lgdOd
JIZylwCN5KE1gbYA7fubvCRxydFcu8zNWLH9SQwpqnKRmETZXD.ndKV5Bas+
VWIHzpIIlWHpyx17CdaFdCig3TowvspDa5oJI.qnBAVvnXJiPnZW5vWN8vsL
uBtIN3+5Tv9G+7vKbFnq0671CgSw2P.RfNFkEXoffE5ZozxAor6Wo1EVoOs2
6rK+HduSoamRSniA4K+7dmReknsQO84hqNGdivf2IrtNzjbK9we1UFQnfsBg
jLNEIQoa2gY3kSCVw+3i9qCdY3HU20R3J1+zZ8ey5CZcOLhBvHLQTnJXClWu
LdgUvlwDGxc53kV2ZZR6kIo90bgfk3JONCBabvoV0sIGrV.9ulzWckLkqQB0
YIVQEpuFuW2sEByu+rwhKu.hTNNRELDgAHRBf.Ilj6..3PDfKDJzCKLwSO.o
OFHnPH3HJmUKFQeUpzDzdlYl9OOTPwpQOlj0Q5wRff.TWzNWIP.FiATDhyo7
IchQb7HHvnuzHYt5cbshDcMypmKUSy3EMRi2EGkngAbGKdfeIMybUr.A709I
vzpi5U6AprEvYG0iGAG0ir5iRo2bTeEG0aqGnPa7dIr+gIw27S+M+zeyO8ie
LJJm9XTDQM0+eLASATQZnrmlsJycHK1c8GO5v0EimC2FTh8XLvH6efKl5M9w
fsJsA5HP5vN3u4Vc+pyUnXhvxlj15mHyay45x7KA7pCkPk5i1ncFestlXx8F
QGrP5n73hJh+DGJ5385bGg4Hev.yAZv.KFej9e2CeAMeZvfaRvsnnS0bCsrZ
LsIAWGTiJvjHMBxgHoHKKkVPg155iGzJE0k6mIN0zUZbMa9gFce9+u1R4QEy
aZWyL14kiK+OD8gfWxpP9WYiLYbgOxJtDxHPcQaKMNbIbptz1iDHBGvAXCGr
X4zeL+e7hGDq61EicSDuoCNXTQa8ASoPaDUxYTSZZp6gqTcMzGPgbJOsQSKV
LJ3XNR8Wz53bYHtP7ph3Y6uIglRyFkozY.l2Ue.KFX95jOPYSk7An00KKBIB
W3weQFiyJLEGf1MV9ZrKWf6uYYQpJ7hRXF1BJTRweg2yKbtJ3Josm2BvW2Bz
aCGt0DmREtWYqmzDeYnskhBuwcgqODoTQMkMBJsYHuN.AEZ0JS6oV55iERRM
JKUb665uu4D9cdhSYyMSKw0drfXJKw1jkvfv4CvTN6qecdnQYydSLw0TpaJI
ctdTtjIsljiGh3MXWh2RK8OXktaPs9vzzNZlPp8aAop3sFdeyI2o6ybCSvrJ
eytiFzo89lzlgjqcmpwpd0eqpEeqpEekz1aUs3Lh2spV7s5+3hOhf9jZXuIJ
d2KMG6OrFRR27DVVG2FkqEzoULY0qzsVAyJGRotERamay8c9BcZgW5rRa1GF
5SK.AjvB58PMmZCUf0cJCotIYXsjSuTFWnd1b2Cm1ro.FW1ack8wAc7B7J62
ZtyA8X.PKoCNpABA4tdA0RGTXKdOrcOG1LLm1H4MsHWLmjBol1.AjNiIWt5F
d5bVIwacPhsKo3b.rIul3Wqo5PYZzrAQ8g.+9Kb1f15SHxlv4BrwMqbcvubK
d1tEOa2hmswNd1vSe7rg4zrtg97G.aGeTsx4kAeHHnRgTSrZZlNkeqQycOd0
C1noC4UMiMIXe5xdtMHk59LQPu9ycHq04NM0VtdCHKa74Qwrurl4zqdlyrYD
9WbybR276uE5vbmlFipnWI9caqp3kpGAd0sycN23gNEjH2OzvIS8bk2uIS6o
pCkAeH8brAxILntQzQ4XNFPrIkHkMug3fBRTe1tUNBzlT5mTvBxZDB3UsKUM
aaZkA1VUNwoE8nYNHHGUHSNOHFwdJoXaQjgtbBaz2N7jpmBdciPIfIzPwpEu
BIFI.fyEs.HjrbhzccKk4EOpZz.nCOVk4KBPV+4nKIubsCfPPCHK.Kl.aLEi
IDHv7qgCyutQamt7i2FLCAKm.H0OIZ8CCWRAgMJgRlkWDo.IjZuZDIEfHrEi
TT06Kb2ocunUCb3nDdLQI0NLPLUdtDkHVNwFakZW4nk4FCChfJFIZSEyxVyS
1lbtX0SfqEmL1liFq73M4vwVb5X6NdremO1lCHqbpM85HxJNizghfYqNjzAm
R5hiI604jN5fxNbRoaNprGmU1qCK60ok833x9cdYuNvzAmX5hiLGhyL6vgl8
5TytcrY2N2raGb1oSNayQmM6ryVb3oSN8rYGeVUBRU4y0teuUQvlJpYsThcx
Cl3lpffc6Nktr.qMI2Crnb1gd.kCFfRazM0.ZCk8pQDF4N.i8WDOWlHWaEBq
QD9nN.eNTzOWl3myEEzQDPQN.nNUjPecfzZZwVFQqVDQ61MTclhMcmQ60cYZ
qbmCsnhtHA1tJ5ncauPMPF0KH6DtdAEgzEIxNZEozgQFf7dICsTvR6gQe.Es
zEI8nghZ5.QVZuHaCFF25AxM3hbZGtQXBQ0pI03.EJLEXVGE5yNSywZIzWaZ
HVO49Vt.rXbj55PQTcXXam5PVuDlLg.bs7McXJVUpfq1CmoqEc0Ny4zZPY+V
F5DCaYWlTKyTqCyMkoGM.1NTbV6JkPIM9lG8pw3fXvt3YNc.ybH0gY90WDWm
mIdsh6ZWjbzWSjbxPH479m4sWDXmm4CZjmOewPIaYx3ZAj8K4Y9PDaUs.y13
NMeSSmxQeoWR0i34h7Db2oaRSobR07UZN5wGqOE6WHV+c+j4PfqsEeXOyHFT
YcMWH4RR1AfKWNM3icGShz8T5iuL3FGCTd4AGW6MlaJDtRa2aluHz3EmuXvq
mB1+h26xXpzDYvHWALG1Q9xTvERvnLv4ZdFD4PrBAuc.vmO.30GOrRQYa4Pf
4Vm4Z80nzFHVTB31Y.e6LfucFvK1y.dcPkv5ninAeXdBhcANBpiE10hut+se
RsvwHbZ8+tSGdm0qwPi7IVpihwzg6HDa.ne2+2PkRAwe1ytN8aUbLe2+5eK9
us++mNHhyUn5a0gknwzI0m4eDZeK5+Et+w3.sKU79iFMr+C4Ox42xFuu09o6
8u8GUp39u7u383y5TN3aC2C+Nu+n5A+tey7lSReapqXe5j+o9OAaOF3U3S4e
2C7cdUehzW7VuuMeH8cE+P9aI5eqHdP0aN6Sx7c+ud2quWzC2WpXROPOnOR9
pLk0Bc1Wk86qbvqO1ols8Adc4GR2Wzorlz.M7q+vYlMmFxFhIn8ZA5DXi7.F
eLGrPtzVkqC0YN+6KUTY.qLkWkreU1q.ip8pc0eSOtM5SdA6+XvVkRSiW6Lk
zawbLa0MaEsZjZSswJ+hn+utQoY0TfOjdvGJ3KB3Qa6gVrWRjm+GiBW6sYqu
5cu1yOIw+wOLhMHWLt2NpL2VB4gqXHAlxjXbpoooIKzh.wN93y6BSTHlxXfm
TV+NdcP3da4zo4ELgBU+kBQrrhlZJy1xnUb+vwnsmRB71GEpz1Zy1nnwDjn8
lAIPSX+qfDIRopIhqKWKVXhwVb8oa05NkJlQeLv6+36GQTpul5NjIVYijA9J
JEjV0vSaCFKglRsxxXk3GuMWPmoFwbnENb+j2KGxfXBTsZkCkDDDKXb6V.bt
Yk7hv2jIOGGb74WT7BdP5EzwLjKgDyqoTWFhY82yLloDFKNRKRzB0dYa5ZjM
llBPdlD4tUzukr0smNy68M7uIiRfoBSTqpnDTffRxO2BjKIgJalNeGqOk7ie
RGWeVxggwek26rji2xAduKkl.U25cZBisDPMXQQXX+zGWoGUKmksWdDuXJWo
MCvtzCIlqcK+nRkhcAdqC0UxL0aRMm7FwsKmrliBxpwlRY1U3p4lMGrX1sUo
y1vSlU4hPvOjgVg4BFCotfs28LyG62E17x3i3w4Q.es1TIfB2XCXRRMqF+Bt
UR3ZWQfvlvBCs6slAoMVallNTf5yF4BTfmtNFc6kCcRZX4RvqvHDJspmCSyw
7hWcBgGrKvC7UAdrEFGnjWQmPIpyqaMhGZSIcy+aBgOmJ94rt5z58nPVEPIU
yJrT8GERBxJM9bcjglZYXC2bBQ.mJB8xoqWy2ACDfaqAfjUBFfoepyArB0l3
g0u2DAUTfqbKv4tMb37PyJrdV6CGDm6wLfYuGy35PCN6MVEBeHn17RPccnAY
yNpwbUyoorkZPbsqiz.+W01IELMU+U6Tx0YY.DQ.HrIaKybrR860Ljidkmr7
14FvutCMHY1WC47PCO6qgbsivjYlxqZi2BNoMdKnq8PpwS8PNznxCjnTcFIY
LLHuqDaUbr1clpIuqMMP3T1+rXKBqPQLm6USRqAqSyv.Lr1I1Tnr9zzDpjNh
uMf.K7YlqJKQlxkQP3fVFglrNQm7p6GdekzSbZoiiDr9ofWbu6rfZsEijUvT
J2bVZp3OaK7QJioYpsTDPQl+SInYqOqnC6+iOGk7hy8VENrm9RSmcwGqmgU5
MWYJ2P8xsgJ5v3Lmsgtr6yXF9ZZmLoTYnn5TlNecSm0Aa8+7KdTHR+Omm3nQ
XhiDnpSbIb1H0G8e1c5bd7qbMSWBnFqM.UueBMpS9f+wZ+GaYdBpOOIcz+mz
kcEcl7Qwot+zz3mPFARDZ1uGugdVtljMj9XXvmL4QT31vjOWjPEsYiNR.r83
.ivh2V3DbuylKWqiUaPjmWMYy8G2F93GRdNN5zSOW750RSmx23i0uwCOkU1+
yuxYaBSuhNDGRYadmNU7L6EDe53yAGW4GtQ+ee21nnCZ8tzu9iQaKt0bK7o4
6EtNXWjZ688qWo.xC9YmrWSUfuqfOFRsU5XiOPDx1H4fBjby6n7gwY4.ppqR
JePcydJY2SapmTP+jZ7Zt9MQHt7MAFiuoRdVsM63JX.008M02bBBGguIL104
z0hdXoCyIrXL9lDt7MwFiuIlKeSzw3ah5x2zXrdh5xJ2JKuK6KsK9qFRbfYD
AGgIIzEgFr4BMqLZJ6zzK9qF4xWMCOBSRjKKCPSBKCxIoJiAKCxEoJnwPRIz
k4DZLjTVt9tz0BtqcelxGlaOmN9U8M4BuHjNVfW+aROFnmSLDrQ3KR3hLC9X
LkbZ0DeD9h3tv4QGCwfbG9hjSgPPtqX4nuGCyE9EDZLT.h65ZfqVPuKeSYCm
oeeyJ7uUNmKcgYVYamxPuJdp.Ka7FitRFnALEtNwsXjqzkxhLr1xVoX4n+Vp
TbbpTTbpWLbZuH3Ts32XbqtsnbTw94yQmstJW9yl5dv6+of8mrl4mUGYJiEO
7zlvsayOglhNjOye.omeSdGet3o4fjDHTX506PLGwLuBqCzqhmVj8YfYODgR
j.yw7PXHNgZdk.go3xchS6igN+cAfR62.PJrUtQr9RvxGY6c96eJs5yvKz6e
ODGcHJNuL8rBKye+mRhdJ1ecXZvECJcnA2mxJoSqgp0Bk61o.zvrmoHqWNw3
GO8neWDffslJXQITUGt7bCrH.Pgv9JBEfPklo2cT8vlJQc4mFAXRCMAx3HKl
J..Ho7Cqc9o44de3dM6aPNNikHnAmQ4DJDWPvkw4xLBDNEhMCTjDKj1WQgpQ
R4GxE9sbZQKrbNxFTloSjypI3.pPXeUsYUUlNFEXwRl9Gt8U1OoIgmKGlJen
o.BjaJjAJnkv4VFBjzxjPY4vawrzPI15OeHXu2O6u+n2OGrK7gnsqO6SO+Ge
rJeGVSvM3IIaZqWKSJV8Dt5EDevzC7h+KZwccsvnZ7YT5WizXYm+uxxKmPMO
LZcYRagLRYZWkgNXEsKBfkqLe8GOkAebmvExGldQd0BakbYjrHqfVZ.gBG6g
0tfiG8eJXroEWp3lBBNxV3W+UcH3fKz6nceiup9isH2iq7pC2nhESiHG3tLz
rB5Cnly3QUZi8.+9SaOEjU7jZdT0jzPHmQrR7yGfR8kfSv.LN5S6G7H773hl
u1DOQivu+y9Ce.BPpsqrhx3XLxt1AAgP73O.+g3ffKXDZPOr9GqFSZ8DF+A2
eMX61nO08nqXxW9KlzujJA.Qm6WnTwhakr.EPi7OpBwoi5Dvz70+AktCCFb0
rh.C4mp1RiZFovhRyFkAXrUgB8HzY4NLhxxUYJ6HOUgJEvIwcrW9bMgNOe5F
wqnhHGBLCHIlv.xTUD4vx6275QlR7OzG2eAMW+Q+jHueTWNOu6R99sFl2ROA
8a9mey+evuDDdA
-----------end_max5_patcher-----------

tremblap · May 26, 2019, 8:49am

Morning!

This is a pretty cool patch! I’m sure @weefuzzy and @groma will appreciate it for its comparison of features and

You got what we’re in for! The whole point of more freedom is more responsibility. Or the other way round. A solution like @a.harker 's (or IRCAM’s or any other single-object) descriptor object is relative ease of use, at the cost of customization. If we rebuilt that, it is a loss of coding time, since they already exist. Alex is working in correcting the few values that are wrong in his, and you might be happy with a curated, single-object approach. But knowing you, you won’t, so you need more objects to divide what the blackbox approach does for you under the hood.

good to know you really don’t understand this sentence, as illustrated by the paragraph that follows. What you consider ‘normalized’ data is ambiguous in your sentence, but if you mean from 0 to 1 between 0Hz and SR, you are right. But that is more useless than just bin, which is converted in Hz in the helpfile. @weefuzzy might be happy to know that this is a source of confusion, as we were contemplating having a Hz output (but again, think about how irrlevant Hz are to musicians. MIDIcents would be probably more perceptually relevant, but then, you need log calculations, not just translation of the results, and then you start to see that the cutoff point of where to translate between units is a sliding scale problem that is in the eye of the (more advanced) user… )

as discussed: if you like the curated single object approach, use it. If you want to have finer control, you need all these. what would be even worse is a single object with all these powerful parameters: about 50 of them, imagine the box size and the helpfile! Actually, you don’t have to imagine: IRCAM’s has a lot of more config than Alex’s, yet let statistical power than our approach, and I can show you the ‘help’ file… and then, you are stuck with them in that context, which is one of very many sub-usage of the 9 said objects…

You are in Max-land for much longer (count the steps in scheduler on the left, there are a lot more). So that comes to a cost indeed. I think that this will pay when you start comparing description, and matching , but keep us posted with your experiments. One idea is to bufcompose before the stats. Another option is to match settings for real (I got down to 0.64 ms which is still worse but 40% better just by matching the pitch descriptor strand to 256 and 7 frames…) Also, you are getting errors with boundaries (check the max window) so there is something wrong there too… that might take a bit of CPU

So as I said, more flexibility, more power, more responsibility… which mean that swapping code order and features and exploring their CPU cost over their qualitative improvement (for real, in matching, not just numbers, as it might not be worth it- or the improvement might be astronomical!) is the next step.

Also, don’t forget that some of our code will be optimized further in the future, but nowhere near a single blackbox object for sure!

rodrigo.constanzo · May 26, 2019, 9:51am

Is that the case? I can’t customize what descriptors I want now, I get “all of them”, and same with stats. From you (and @weefuzzy) have said, this only costs “a bit more”, but perhaps that adds up?

And the main thing I’m talking about is a 1object vs multiobject paradigm, this has nothing to do with open/closed, static/custom, etc…

Is it technically impossible to have specify multiple fft settings per descriptors in a single object context?
And does selecting what you want or don’t want not count as customization?

(it would probably take up less space than 9 objects with (50 / 9) parameters everywhere…)

The overhead of multiple objects doesn’t really bother me so much. It’s more coding and harder to read, but my main concerns are over the ramifications of having it be multiobject.

/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

(I’ve separated this bit out as it seems like an important question, which I didn’t want to get lost in the rest of the post)

Also, this might be a naive question but how do you synchronize and time align the output of the realtime fluid.descriptors~ objects so you know that the frames of analysis you get from each correspond to the same slice of time (in a single vs multi object paradigm).

This seems crucially important for realtime matching (and any realtime use actually).

/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

I checked the help file then (and again now) and I’m not seeing where it is converted in the helpfile, or even a mention of where it would. (ooooh, I see now, it is converted in the realtime version, not the buf version. That should be everywhere but…)

For me, lower on the list of “relevance to musicians” is “spectral bins as units”.

Definitely. I would just ftom 0. the results if it was Hz, but that problem is more complicated for centroid as you guys explained during the plenary, so I appreciate this is something the team is working on for now. Having a “proper log” centroid would be ideal.

Even if they are not properly scaled, having an existing unit (Hz) as output is preferred over something you have to manually calculate anyways. (just like the “latency calculator patch” in the previous alphas)

This is not a snarky question, but is there any benefit at all to having “spectral bins as units” as an output?

That’s one of my main concerns.
Regardless of improvements to the algorithms, this will always be the case no?

I thought about that, and may still test it, but because some of the descriptors I want are not immediately after one another (centroid and flatness) it means two separate calls to fluid.bufcompose~ to get those items together in a buffer.

Is there a reason why fluid.bufstats~ doesn’t take frame and chan messages? That could save some steps here too.

I also thought about comparing peek~ vs fluid.bufcompose~ in terms of middle step to see if peek~-ing is faster, but I presume you guys tested speed things like this before.

I’m all for more power, but power to me also includes speed and efficiency, especially for realtime use.

I will experiment with a couple more permutations of this, but my gut feeling is that it will never be remotely as fast as a single object and not because it being a “black box”, but because there have to be so many steps in between now, with each writing a buffer.

I will test the output for sure, but if I do so I may have to go down to 256 samples (instead of 512) since 11ms was already on the slow side, and 12ms just makes it too much I think.

tremblap · May 26, 2019, 10:16am

not in that realm. Max messages cost a lot more.

it actually has all to do about this. interface is everything.

With a readable interface, that is the case. Imagine: take the interface of all the objects you are using at the moment, and make a list of them… I wanted to take pictures of the ircam.descriptor ‘help’ file to illustrate. But there are 5 supatches made of lists, plus an expected multipage reading dumpted in the max window, with lists without comments, on all the attributes available for all descriptors you might want to customise… and that is all with a single FFT settings!

Granularity of code vs cost vs customisation is what we are about.

that is your job in maxland, not unlike jitter. your implementation at the moment is sync’d, but you could have more resilient behaviour by triggering the frames as you do then collect the stuff at the bottom. this kind of responsibility will be even more on you with multithreading, that is why I think we will go with it off by default, although in the light of this discussion @weefuzzy might have other ideas.

this is where poweruser comes in place: try both, see the respective cost. bufcomposing is cheap compare to anything else.

go for it. peek is a very small subset of what you can do with bufcompose, so there might be a cost there. It is so fast, I did not bother to compare.

I understand. Hence my recommendation: if a curated imperfect blackbox that is cpu efficient is better for you, go for it. what you have not tried yet is the real power of the better approach: matching with something else statistically. then, quality might justify cost. I do that all the time with plugins: sometimes I need a good EQ or reverb or distortion, all of which are many orders of magnitude more CPU expensive than the cheap, OK version. When the latter is good enough, and it is many times the case within sessions and patches, I go for them.

rodrigo.constanzo · May 26, 2019, 11:35am

If you want all the granular control, then you could have granular messages. But if you just want a few descriptors and stats, that would be a much simpler and readable message.

Either way, the interface isn’t my main concern. It is annoying but does not mess with usage/performance.

Is it even possible though? That’s what I’m asking.
Having an audio input and analyzing it with fluid.spectralshape~, fluid.loudness~, and fluid.pitch~ and putting those together where the frame you get from each corresponds to the other?

I could pack/join the lists together and slice it up as needed, but there’s no way (that I know or understand) to make sure that the frames of analysis correspond with each other. So I could be looking at the loudness from one frame, the pitch from the frame before it, and the spectralshape from the frame after it.

Or am I mistaken here?

You keep saying this, but I don’t understand what is more “blackbox” about descriptors~ vs fluid.descriptors~. Nor do I see a “blackbox” object being a bad thing anyways. And as I said, you can’t actually customize what you get from any of the fluid.descriptors~ objects, so that’s not very “poweruser” anyways.

It’s not a good comparison because what makes an EQ or other plugin better is a better algorithm which costs more. What “costs” more here is the architecture, not the algorithms. Both objects could spit out exactly the same numbers (if they used the same underlying algorithms) and the multiobject approach will always cost more and be slower, with no benefit.

(to be clear, I mean that there is no benefit from the architecture making the process slower and more expensive)

My questions and concerns are around architecture.
I see the benefit of having control over separate fft setttings and parameters, but the cost (as it stands) appears to be too great, particularly if the choices are between doing JIT and being slow/expensive or doing realtime and being inaccurate (frame synchronization).

edit:
I will try making a more comprehensive comparison patch, and will include the resulting outputs too to compare it in terms of “real world” usage, that compares diff bufcompose-ing order, and peek~ instead in places.

tremblap · May 26, 2019, 12:44pm

yes it is. like in jitter, you have to decide who is the boss. When we go public with the multithread implementation I’ll put examples in there.

to a certain extent. FrameLib allows to do frame accurate at (multi) audio rate. What you do is ‘slow’ in computer terms, and easy to sync when disciplined. I’ll do examples. The main hurdle here is that you still think in parallel. Computers are mostly serial machines, so you could recode your example and take the chain of events in total control.

and interface. FFTYin is more expensive, but better, than the pitch in descriptor~ For info, ircam.descriptor has it too, and is more expensive than Alexs, but for many other reasons too. Implementation, interface, quality, are all intertwined.

For instance, forking in code has a cost. Every option you give to a user implies forking in the code somewhere, at best. Architecture is everything in code optimisation. For instance, asking for different ffts in alex’s would raise the cost significantly, since one thing that makes it efficient is Alex’s clever order of manipulations to compute as less as possible. This comes at the cost of flexibility. That is why he spent so many years on FrameLib

Then use something else if the cost is not worth it. it’s that simple. But first, try to compare for real, i.e. how better you describe (and more importantly, match) when you have what you want for real. Then, like plugins, pick your best cost-to-feature ratio.

if you do that you need:

description
matching
matching with what you gain with the 2nd interface, which should yield better results

then you can compare implementation cost vs results on original task and aims, not on using a ferrari to plough a field… which is an analogy used in other threads. It is an important one: there is no free ride, not object/architecture that is better, only some better for some tasks. Here we try to help you find the knowledge to actually compare that -what you think you need to do a task you think you want, then by having access to the tools and knowledge to explore all your dream implementations, finding which one is best for the task at hand.

In other words: in some of my composition patches, I will still use descriptors~ (when it is corrected ) when it is the best for the job at hands. What I learnt through the fluid* version (and the python prototypes, and the audioguide software) is that what I thought was better is at times worse… and at times has better results, at higher cost.

rodrigo.constanzo · May 26, 2019, 2:44pm

So are you saying that something like this would all correspond to the same frame of time?

I thought the right-to-left thing only applied if you stayed in Max land, but since each of these objects was getting audio, and their outputs wouldn’t correspond to right-to-left order, or a known order since it would be different each time you loaded the patch(?).

Yeah totally, but I’m saying that the present method/architecture has an overhead that has nothing to with what you’re discussing. Going in and out of Max that much has a baseline cost beyond what’s going on in the object(s). I’m all for fancy algorithms that cost more, but this isn’t that.

I’ve started coding another comparison. I’ll post it once I get the right values out of (it’s not producing the same results for some reason), but I’m slimming it up by pre-bufcompose~-ing only the “rows” of descriptors that I want before bufstats~-ing them.

So far, this is actually slower than the previous version I posted (though going to the same fft settings for pitch speeds it up some).

As I said, I’ll post it once the numbers are correct, but the amount of channels and such being processed is correct.

I wonder if having an extra bufcompose~ step is responsible for the loss in speed? (so perhaps analyzing “all” the channels is cheaper than separating the channels you want to analyze first)

tremblap · May 26, 2019, 3:05pm

definitely not, as you are streaming, which is not what you do in your original. I would go through a buddy object, as this is one way Max-land has tried to help their idiosyncratic timings… but I would do like you do elsewhere (inspired by my JIT buf processes) and process a given windowed buffer.

I know that is what you say, but I tell you I disagree. It has everything to do with what I’m saying. Flexibility and power comes at a cost. Your desire of a good pitch (fft param A with algo) amplitude/loudness (other settings) and spectral shapes (another setting) with the bias of window size you want and nervousness and post attack, all 3 having different consequences on what works well, would make the blackbox approach impossible apart if it is coded for that specific case. So you need flexibility. So you need the current architecture to get it. So it has 100% to do with architecture, because, as I said many posts ago, these are all intertwined.

rodrigo.constanzo · May 26, 2019, 4:40pm

Yeah totally, what I’m doing (for now) is better in a JIT context, but I was pointing out another (potentially big) problem with the current architecture in that if you wanted to analyze in incoming audio stream for more than one type of descriptor, you can’t synchronize that data (as in my screenshot).

It’s another place where this architecture is working against the ideas you’re putting forward (flexible, powerful, etc…). If you can only do realtime analysis of one desriptor type at a time, I wouldn’t say that’s very powerful.

I’m actually quite curious what you guys are/were thinking for doing matching in the future if it’s not possible to synchronize realtime objects (would all analysis/matching be JIT?)

groma · May 26, 2019, 6:13pm

I have not read all of the thread, but I will comment on some of the issues.

First, the descriptor computations are interrelated. In order to compute the spectral spread you need the centroid, for the skewness you need the spread, etc. So you will be computing them anyway depending on what you request. The algorithm for pitch and confidence is also the same, there is not a specifically added cost of computing the confidence.

About the interface, I think an important point is many people, even people that have a good understanding of the DSP, do not necessarily have an understanding of the musical utility of descriptors. By grouping descriptors coneptually I hope we are helping to make them more easy to understand and use as well. This interface is also easier to port across platforms other than Max.

I get the point about RT synchronization, and I personally don’t know enough about Max scheduling to understand how it works in detail, but clearly with sufficient overlap the evolution is going to be smooth, and the descriptors will correspond to approximately the same point in time. With this configuration you can also use different STFT parameters for different things. For instance pitch may require a larger window, but other descriptors you may need for rhythmic events you may want to use shorter windows and less overlap, which I think is also interesting.
We have not considered “matching” too much as a use case for the first toolbox, as it woud be more of a topic of the second toolbox

rodrigo.constanzo · May 26, 2019, 7:32pm

I totally get how splitting them up into categories makes sense, though I don’t know that the general level pitch of the project (“being able to code a granular synth from scratch”) would need too much help with descriptors. When you’re dealing with stuff like nmfs, audio descriptors seems like the least of one’s worries!

It is also possible that one can use multiple fluid.descriptors~ objects at the same time, going to separate parts of a patch, so synchronicity isn’t important.

I only brought up the realtime (and later, matching) use cases because those also seem to be problematic with this architecture, on top of the problems in my specific use case(s).

So at the moment my options are using JIT (with a 10-20x latency increase in computation time) or use the realtime objects and have decorrelated frames of analysis for each descriptor.

tremblap · May 26, 2019, 8:23pm

This sentence is wrong in both cases.

The computation time in JIT is 6 times, not 10-20, for similar features.
For RT, you just need, like for any real time thing in max, to be discipline about sync. I recommend buddy which will give you as true as you will get.

Continue your tests, as now, you can do them, and see the cost/benefit analysis in your case. As they way, your mileage may vary.

a.harker · May 26, 2019, 8:38pm

A significant part of the cost with the flucoma objects is the pitch analysis (c. 3 quarters - this is a more complex algorithm, although having read the yin paper at the time (when it was under IP restrictions - I don’t remember the complexity being so high).

Another part of the cost will be due to 64bit floating point calculations (rather than 32 bit). Another part will be calculating much more values at various stages than you need each time/calculating multiple FFTs, copying between buffers and so on.

I have already criticised the use of FFT bins as a parameter. If log freq is used these actually won’t be relevant units any more (nor will Hz or normalised frequency). I see no reason to return in these units, and indeed I’d prefer normalised freq (my preference is nyquist at 1.0, not sr at 1.0 but there are in fact two variants of this). Returning in midicents is not relevant to the outputs other than centroid, as they are linear valued output that cannot be meaningfully translated in a log representation. As the sample rate is in all cases known there is no reason not to convert, and indeed, even without the sample rate you always know the normalised frequency. There is absolutely nothing to be gained in returning these in bins.

Finally, the main additional flexibility here is being able to take time series from the objects (a feature that I had planned for a future descriptors version) and you have the power to deal with edge cases as you wish, but I do not believe that to be particularly useful. I am sure we will continue the conversations about relevant interface, particularly in the case of edge cases, but PA seems quite sure of some of the decisions so far, and I have a limited desire to argue out these points, given my investment in this area for many years if that input is not wanted. At present I personally find the solutions to be technically, rather than musically centred in a number of ways that I find frustrating, although the grouping is indeed helpful.

rodrigo.constanzo · May 26, 2019, 9:54pm

My original test was 1ms vs 0.05ms, the improvement left it at 0.5ms vs 0.05ms, hence the 10-20x speed difference.

Unless I’m not understanding, I don’t think buddy does what you’re saying.

buddy will spit out once three messages are received, but based on whatever the slowest message is, so it won’t be time aligning/syncing anything.

Do you mean something like this?

Does that work? I didn’t think signal->control worked that way, to allow syncing of data back up.

Now that I understand what is happening and how to convert the values, I don’t understand the reason for using something like bins in the first place.

a.harker · May 26, 2019, 11:12pm

Real-time values from these cannot be relied on to have the same analysis frames exactly in all cases even in the case of the same hop size (the other FFT settings are irrelevant - if the hop sizes are the same then in theory the values can be aligned).

However I think that if the hop sizes are the same, then you probably don’t even need a buddy to get exactly synced frames. The reasons for this are fairly involved, and this assumes that all objects receive their audio input from a single common source. This behaviour, however, cannot be relied upon. As soon as you change fft settings on the fly then all bets are off.

tremblap · May 27, 2019, 7:07am

In fact it will, like in your live example below, as long as your slowest computation is not longer than its period, your certain to catch the last valid value of the other 2. That is, as @a.harker explains, if your fft parameters are of compatible values by design . But for RT descriptions that need to be sync’d I’d either use JIT for time series management, or other options for short, time-critical, efficient methods, like FrameLib or descriptors~

Indeed and that has been taken on board. It might even be actioned soon but that depends on the whole reasoning of all party involved, and often times, I’m explained good reasons

really? That might have been my call, as I thought it was abstract as hell (and 2 variants made it ambiguous). Interesting!

a.harker · May 27, 2019, 9:04am

As mentioned elsewhere I only prefer normalised frequency to FFT bins (not to Hz which would be my preference). I could have been clearer above.

Normalised frequency is common in the DSP literature. I don’t find it abstract at all and it is much more likely that the user knows the sample rate rather than knows what FFT size they set, and how many bins to expect from that etc. But Hz is unambiguous and easy for all to understand.

rodrigo.constanzo · May 27, 2019, 10:55am

Another interim test, with surprising results.

Having no bufcompose~ at all, and just using the frame/chan messages to bufstats~ is even slower for some reason…

Left to right is the vanilla implementation from my first patch (analysis->compose->stats->peek), a version with more curated bufcompose~ where I narrow down the channels I want before stats, then a version with no bufcompose~ at all (analysis->stats->peek), and finally the vanilla descriptors one.

I also tested @algorithm 1 and it is faster, but not by much. It goes down to about 0.3ms average instead of 0.5ms.

rodrigo.constanzo · May 29, 2019, 10:13am

Ok, it took a bit to fix all the numbers and to make a more comprehensive comparison patch (we have friends staying over at the moment), but here is the patch.

The processing time of all the fluid.descriptors~ patches are all pretty slow, but the performance of @a.harker’s descriptors~ is quite erratic in places.

Have a poke through the different sound examples at the top and look at the results at the bottom to see what I mean. (playing the same exact sample of frozen noise is a good example) [is this due to what happens with edge cases if the fft size doesn’t perfectly match the window being analyzed?]

There are a few blue subpatches that you can see the results in different ways with, so have a poke at those. You can also play the frame that’s being analyzed. In order for this to work properly you should stop the record in the main patch to make sure you play the correct bit.

Let me know your thoughts.

edit: updated code to work with alpha07.


----------begin_max5_patcher----------
18847.3oc68s+iiiict+bO+UHTYQvLapwCe+3lrK56N4l8FfYRxECPBB1Ing
pxxU4scY43GcO8tH8e6WRQIa8fRhRVRVUOr1cp1krkE4GO7vCO7bNe+0u5M2
8P7uDc3tf+WA+of27l+5W8l2jbI8EdS5e+l6dI7WdbS3gjO1cuDc3P3SQ2cu
48NF8KGSt9l3vkODt8or2X6oWhOcbSzwjaCld00KS9vwO7m+VHQj8YMevieZ
Wjokb2cA+Wou0tviO975sO8t8QOdz7tP.Bu.be.Bgz+CUX9iEfy2j5Yuda1i
FkdwGd5w3Mw6MeGfERACQX.hj.HPB4d0k..NDA3BAPxwBTxkPbBl.EBAGQ4r
6Cf4dLoeinA7q7o8gKWGs8XdL6gmVsdyljG06xvn6L8EKehjeCGzdYou7y82
j6M6WMdKiTyQItsIANPb0cY6Cb5XbdH09mZ293cw6OtNdq4yP0ej+mu5qz+5
dGmVrM5iJg5JyJN975CIhvQ6sOw.YahAs9IF265jCJW+OLdySNxjwND9grGw
ey+h9gTngGb2eu9W+M++z+NZ6RyUB9u5ANUm5iCGC2eL.nZ+p+u65Pfr9oCg
SjKXPnDKHbFkgn2Gf4KT+lSaQaxHzmQ5+eG5y7AqOyjiUetl4C61D9oOGrMd
8gH2mO.qeghCqeZa3Fs7XxZOma+qh2d7v5+RxGChNOqWe4sguXTg9+d+Z8s1
E7RxzHEG0xjpgCu9seNftnChFz1PpNJf.wI8XLYxjPND9wOGv4cX9.FbMcZo
.sPB4BBiwkLA+7nLCLcZB.5k8nATkx.p68bInuFPAEk6yzjUMlRse8rOC6We
VYKwhwseVi77l0ai9r655vvNqqy5HLUZWpdBmI20dNZH54RjXwMo29a+bGzX
gGXMVopoMc54W2kbMcWgZJa993zMj93mdbSzmUpN5xhQzgnultBDa5sEsa1e
K5oonLkw1krzX5W.peleKXCVWVBm30h5i02b9Mw3aKvUpBfwv56FrQAsHwDk
trAMbOEQnDld6XzjNnZqYSqoX.X26n8b5OgBuzQEnYqEIhgvhjzQUyj8Yr4W
xgnylNxN4c1tXNB4p1uHEjnuNUYjZShz4XWDdMcQRhR6rtHULYcwcq29d26k
W299S6joRphgeMkGNc7X71NLlgtrb3d0xjGi1+tnsgOX7vMnttZqSIwDzBp.
KoXo4Gh77NEHY+dzcj0iaV+XGFaIrqYr0ZWNyUVxIy4c6BTMo38K+Wp2fKfE
AAjnI6hTZbJYWDAJVPjRNU0koRLmp6sBbycSP9GQz9zNWZuSYq15MQeHZ+gz
yDIcD5M2EtaWtK+lb2hFR9ylS5Qb+4Ksdq4RfyWZezGVmc+zyWMbupidT0KO
s2Xe3uvx1zn9qIdYz9smVed7IYvIsIkLLnsq7vtvGM2rdzJ6syo4NYjGBRD5
YjD26QDfKHj9Tg1D+36iVluIeW7tnsq2taezgnsGCOeLQYu8xnUgm1b7c1s5
s36uJsEZ8MsZar9HAWuLdqtQTXjPe4rG2eJ.ZVThluyj7I1FtyxMqjyTvRMu
oZeQGOc3gv85ApT0Onr27Xb7lhu046aSzpiousRQ91Rn3w3c0+l6W+zyMbuO
DqdyWZ56N4cN7tSaMu66TxDGeW1gZc4yEtYS5b0he8+R310uDdL53ZyP.Bb9
MMpfe9vi6i2roP+07Nevx6rTIi+XzGWu73yEN0Q86n93q2kIDc24Q4kqeJ5v
whW6X3SGJdkCG+jAzycoSOjNG9cGidQsCuik9.ENg+7SXyqUqv0abWCEUwol
d+R3FkXnpym+CXUAeksVJxeK0uEhF1TYyarrV2iRLSWfI5.3UbyRMF47lrUF
RU.0S7rvpEUChBMbG.6NXJuQfIEXTqBru47ZVgc7wx7FS5NLd1WeNY7wfCll
ksg3DqRIfYCXZLjonui5.nBuofpwUJPyA5IHsCp3oATe3zpUQ6SAU8LHP8HK
pFjE0HxtRoP4XkMtOZ5SMVXmpVs6xsWLcQ66hZVyJ4wqee6X+g3S6eLq6mMg
NnXCQsH7w0aOaI2e5hPp9C5zXeWaEZsyyfVgv0VAp1VQ5Eyr+NIxgV9NiAku
K73w8qUa70L3keCEcxvGkU3ODtI0rlyRhVrK5qtz353V1dL9kWzAEV48rAWD
7Os4z2G+igAeLd+6USf9XvWm03i2e3a+8pYspadW7gnu82qMU9v27yaQKB9o
nMJDX8GhZ69e7zdkMZKCr88fs88j7l07kjcijEA+6JaX2rILH2mKq2oEQdL9
jIF3HtuQTdCaDkZ7YBgCVn1fMki3pcaCLQRkTlE.EC2drqa.SigmLaKKHw7c
a8Yj684yme2Cmdn3NbRuxg0KStJtgiwCjbJdpcjmDdUIwfGlOMgKDzcONgtt
ig1rppv3UkQHXYpaD+ed6wn8gOdL3iqO9bvA8rk3sGBhWEb74nfjVp5UgGCB
2GErdavCaNEs3ZEKfM4SFXpyVLHhwfXLdR7vzS6CWu8eaS3mb2+Rvyd5uPr4
RT1e.HB.AhIXlTejPB8UvTARBYHl9JRoPhkPJihQHgnXn4Zyx.bBzPAhJZIZ
6.tf+J2CUDjw0zTn2CUdOT48PUC6wp3JEEUQp0MlrvfFvMpKW7ya+4sesNB2
SrQ6PvCpNVv5UAeJ9jxhp3L+1qTkoW+PeyuntszkVhU+89Op2wl9iue8g2Gn
iqB8GNLXe31kwujsnT1ZRZcUeS9VWgUg30toOf8M8gZY6XHTdWx.MQVSxeMM
9QnvIe4tWBfHRwNVsmB1YaVRfwgbMLGNisZidZix6ziRrxIqcybcyemZC4KX
c2ghWxshl7sPanBDyyAKDwrwUqGBeYmZt9KG9bmc5xkvrus3kvMLpnGT4yGO
nF936CLQI2J0+quhQvN3X55mWwwKXLFV.SOjSMf4fi6ICHTE8WVF9XChKf5P
.9ctzEQpsKBK1EM9UiPmAyWLwN3wD2E8TmmxfbcFSGT0hfIAeiKGeyPNgI4a
sGSCDCf1TilBy4BfO+65624kHyLRJuoWIMxsKi9kI0krZgAmbCYxLmReRkcY
lMCBGEWj1w1FzdaCLJssDCjbrwMltQNQV1w1AcTaGrNgGnQqcPqHJTW6fUu.
M3l23f0KQCGoFGzwQPD8K8SjXy5ODsHY41z9wGBu7jp7l1Cn2RmW6c179Goo
vLD5VhmWSD9qe7onYwFPmB8eTpcclCAmBcOC1WdY3tgA9ODt4TT7pptG7Bfr
d65i5FoYKLWDOy8QN7b79i0N9TBdO8RJLpd1ZId8+hty52aFlir7dah29jaO
xzNP0Q1BepWTBfE7X4vedZVNMJG7PMnwzxN4TUjF6e3lMZCZS7HuuMGxieZ0
lSqWtnW8RfrgdIVn1uChgDPcUwP.QW5xP.dZ5y0dnCubZyw0G1rVs1v+95nO
1gtLY5N5AIJMdewk2UkzwJFyuVO4AH2TCQnLh+nG7G8f+nG54QOrR87TaZ9P
28XDns.tRoNyTNCfIyXoUVEr1UEruxv.4ajlPiGUuw930K6LZHaALjlPPHCJ
XyenX2ZUmny3fns.wUJWftHSXb797FH1DeZYulgzlGUgRzhKPACN+ghplP5N
ZPaCMXlE1wJKePHLCmZPI+UfZi5Lv1czg3H5.kjBniE6ruMvSNyscyw6GTWK
awPrcPgw65YsVu6.pEXQPVxpTXkIe4QVrvrQFIxUnU0iTuS51U+SA5j3cgIq
0czmd2PjVLgHMDH9ULRKGejVfRRv8AWh1Dy7If8rGm4fICmGb44wBma3jSOs
8OGuda.od7mVi7byAfQBNl++J.HOud4xh6csNOMASKTn4wYFb1DEB8G9nSA7
AoXZhp2aLBNJJTQi+DcBhkrqkeUqQkQlNf9UiJ0w.noSfIBFEpCuMuhWMFhQ
ESEJO716NNnbOCZH.55hcNCHoLkxTFTTXDQ+u3IJ5glZzBecn04kxmD.CMUg
ak1LRcruzZLVvqlpr4LCZTB.jj1F1g1FSbaZatD3LLxsos4RbyPuA3F0Q4Ml
71z1bRdieaZaNIugtMsMmj2l9wzD89N03rzKF+FGx0FGqiMtuPh6sZixDim+
e3zpOzsXLAN8wXBUTNFSDTe8WqTLlfDowgQZUBgXBxDL0GjI9fLwGjI8rBro
1+poNBEjDUAuKohff59lmXW4NMklpbAZgZjC.QH.GfMGiJBMsI8WGPqtiSHv
ffSJcfkwIIX1BS8VnBMHfk52ulDpdmxxqW5AXgGDvBhfUQKH+Knofsj8sNCT
D5qj4fYQIkYZHty.l7JwKbcZ1Iz4Lbg6QRZNDHkEc6PHd9KX08Yhrg.troce
dKX0SE77g.srpdmLqUa0cjRLHHkE86yz4g4Lxp6J2GFCRgBanEa9q0p6J4GD
gKavEVLusIcnKFxtJag.RKxVfYuJ9tu4vgPzxJZgwyuZTNXgryHzUtGGZZ8w
lQzAGnXhqO1q1Dqyj2tV6SHWeTT4TUQICbjIj5L0EvoPvqu+kvjuI1DMq6C+
k33WB9McujBQFFoHjZRU4IZtD0dSUA2IPOSq6fC4JAGjnNvg3.3.J+se4rMJ
hC0dFG0cNGktcam2QMm4Q8m6Q6m8Qcm+Qgy.wgyAoo7s0JWzT6og3vIh3xoh
z5Ii33oizvIj31ojzxIkz5okz5Ilzxolz9Imz5om3vIn3xonzkSRogSSo0ST
o4SUo4SVo4SWowSXotSYw9IsTyos3zItX+TWJq.orN5Juea5pKm.eJCH2D+w
xefZ0Z2j0iNn7t137MkfK34RL3RpravLoBqqUbssQF+NaWYgstr74dimvgAO
w7ylkmTHKku9PzGVuKdS39.X+kMwCCXB4v7klVvqPvzX1Z+gR4vfjEJZnyJf
zzeZFe.MLsshnVs8djfoKJnbHmhjHISjjroVCT2F27SSAraMQ4rE7qnEMUBf
2pvpsXIyB3VIlxpDhcMDUYkHxkNMr221GBO2afcn8Aw2fFHbPAvhBlI1T0hj
4ES8Osbc7OkXb869wnsmJNwHa6AM26d3I0N81bNX7dSseRk11Tkfogt28kd6
BAzGRRfPgNp8vPL2D+dX0KnzRQQat6EdNZ.oDI.ouEBCwIzjWIPXJNM2Ord6
nKOa.TZdh.o.PLuRcIXkpId5dL29T5lR3EsV1HlrOdW79y6haAVV49OcL9o8
gKWmpgBT38KLdWbaoFONotK6lRq0cqS.nxemMITeV33GN8XXeDHh1Do0zVXz
jKT5wolnxDJDlWQn.DxJhd2ACMIEus32BBvjIxDPFGYFKE..jX+KoX.hxoPb
xyEIwBo4UTHPGqn1tYskAIeAua8VsuJhNKbfkHHyb6YRYHtffsKbzq4GmkEZ
YJRGESKNIQbdpgfCnBg4U01QJOIgQAlwBl9Gt4UluwaxbjJvdArRBHPtIrfk
TBmaD.QRiPIkUYXKuKQTph+W2EsM3mB2dH3mhdY8CwaVV0LtvGerrbOFkPWy
pWPxfKsNLRlP2MYh86M4C3+g1sX8YBdtdnwb+7+5F0mTlnxJ4ONW6N0NS2jn
fo+x9T65gBkPkKBHlYcmUovSmHeaAxxInWejLTZbUq2hj4E40JJIT3st6U8T
KlDYkgdYfbJxyT.W8UNnHmKzVFcu0WU+s+EgsR10ZbcRW11ebOl8jHKkyNVE
1huoqZXbfxeXyonxtdysdmsUGgbFwX4v4NpTeI3LnitO9iaGrd5k9G8rtP7L
om98eJb35n.jx7JyRYbLFYzEgfPH912Q+i6ihFvdZxnIV+iYGIZ6ou8cx+yn
Mah+X+5k4OKw+TxoIRk.fvI6XT6khaV4.JfIqCRURBzaJfb5kGh1+GU1pOXC
55orfDwapxjMZROFle0taRGcuwfdcO8pW2gQXLrLcZKOciRpARI1Aafmq.zE
7oeRBk1JIGBR5XRLgAjoakjCsauzLQLJ22Y9uv1Rq3xQ0QuN82lSyXaoZbQS
4FfHw4Oe3z5tGrMWYUzrPbzniGly88BI+7h+7g6bKRKMjgEljFtfSW0Gy8PQ
syfLENLQgpkHZRPligUI.0iXh65vHDTlKvJk3WEAVIRLMAVYF37EefUhfChT
zWlAVI7Jmhoma4CrxQOvJSyvGI0GYk9HqzGYk9Hqb3hrxr76R75OxJsP7F2j
fqDkxtyBgw7SnO3J8AWoO3JmEAWoD6CtRevU5CtRevU5CtRevU5CtRevU5Ct
RevU5CtRevU5CtRevU5CtRevU5CtRevU5CtRevU5CtRevU5CtRevUNrAWIct
Dak3WAgVokCz0wvZZPJKw1hpIHXVV9XOrS012Gt4vyg6h5aIUWbkk5SNkVWQ
Umil8nVOvK5ffWoTGz7rdWaQvp6k9ZhXP.JaEU8WABV8rxpSjCBnYs1pyl+J
w5NfQACCfYqDq+pYxY2UhMPhY1pc3L5qiImcm0C3CBnYspXy.yyz2n6kE6qz
bBYw5hM+UQ5a.mnz2P9qk5hMhNHRQeYl9F3qbIOoutX6SeCe5a3SeCe5a7Zs
vXaLjz3CAe5a3SeCe5a3SeiufSeCoO8M7ougO8M7ougO8M7ougO8M7ougO8M
7ougO8M7ougO8M7ougO8M7ougO8M7ougO8M7ougO8M7ougO8M7ouw.WarYyj
72HKBBm2IvQgPsr6fMbXhxRaowAF2uX+57mt548bABscJ.4.xpmuCnr6+qw0
+5Rydde+23.YmaEN1HniXaP3ZiPOQb7ZEzYQq.MKZEyBYSwLP1jymCiG7Y.R
vlAsAJbNHWRAyAYBxrPqIQLKZEj4fbAAOKZENuBBdLaEvYgbgiMBjXDaDXWG
PfiZqXVnzx4YHnQrQfbUmEBNlshYg8lH3LXYczrvbSzL.IfNKZNpsh4f8lP1
bPn.RmEsBxbX.YVXdCDMKZEyA0lWea3hispFunWZWNDmnV7edtVdMA8PdGJW
eLg123A8JhEzqHNPqOtFZNlFZHdFx6.76+p1c+eUW+aSXql36r4AxqLlNu53
4r2wx4fDGmcRN1VP6z+Pl4JhYy9GulihrbA3r2wlYGiKyqIlLGkIc1i8xlm7
4X7VNJs2JQHUyM0dEebcINJ6aLTNZfSoXkz4QR2iOxQqoW4T8Fmw1gP0Yuh0
wqHNGuBE12J6.pNSs6RDkicwNHM6R7JNJRx1iKwla4WSrHNlchxwbXm6ENGm
giXunb7D14NgywP3H1IpDqfcuW3P7ANhcfpwAXy8.Gh8uqHt+FmNp036qyCT
NGLViRmvZr60A8tcHd85cr5MJcb6wjWG54cHN7leC6GC20kYl41wyODdLN3G
zEjl6Fh1X52Q1WPawCXZ6p13.zVL.1R7+UN1+Ll6j7aCDl7LyEoZl1PCk2gK
Am1os+430aCxJ5SkhDszhEUgxS.HqHaYubsU3+xDwt640KWVnxEYK.0XlBCE
mk7OUKzHVBeuaDHHkiEFPgpIYHFR.4Xg525nyaDAjBk0O6kzuB86yPTCw5oC
kwOW64BL0dwNzZor3t0OsMV8EtY8iuO+kKUd+FYHhNwPDfcygnrRGR44UYkv
nfuVM6Z4e3a94s+71cqUcqjK7i+y+i+y5EcNjb8Mpqn+q8wqWZ4sWsI7X9up
rmk9PAdL9jYGgbqCRfbMq+7oCGWuZ8imOH.aifYA0bAS9RzMU6HClhpajAlV
PqAha4DW9DKUJQu5l3JlXHptB457EhDiHDkt3+qPM9P33iJdk7NpjO+DZT20
iyAPCdymMptIzwW75Un1Z73iJu9TPSPiGpvAfEW1LR0RrscT45590resWhB2
5NlfqcuZJzU01Tc90ZUKNz2QFFcQNY6L8urI3o8wm1ozA4dGl27lScnixLB8
LQycTz3KQSGpEWsWInuq1Z6bSnCAYJY02vo.6Bdb2Ic0MducnDZAJQsLQXTv
Jfw2EzVjjx+Ecorom4LsZJW51JU5Y9EzRIR2R4Q2doQu4xhtsRh9Y2K1RoPO
eYPGkxTHHCyEXVlAyy4XVqk.8VJ+41K84vETIEJXxrer8oKWDzcn.nWSwOu8
BedCE87FK34MVryanPm2bQNuwBbdKE271Jr4tVTyqofl2XwLu9BYd8Ew75Kf
4cym0UJZ4VJX4sVrxqVnx6ZlWWoZBUek1sthisUG5jT6vUaW2TOwy9sa7IR9
Roqxzhn8eHbSfZKMubHX8gf8Q5fbHZYfBUJjE3UqxtiKKa7PPO3ejFyA8pb0
hSZgZbwk5FgffDMlbypvFsmfoJe+a.UUqK+nVqc2YujFg1hKQOMXKDPtvHey
Bv8aUpq5dgSX9.ropUL0mBbxttaFWQyWUAzYip.ik3e4nIfMeDXIFGgcaTDT
tLa5Haz0Q4x50+Q3tt5OnlU+0OsfCJqKODnrdS8PBB2tL0B.kw.o1FX0H.zM
GHEWOPZlYNX3XnF0RAxFAO3TUqZlEUhCWyLxYPMvPmtdvwp5S3XaXNjW8v4P
pjOCRYXbCYf23G2Ut4ijaWzYcLXSvCt6qTxcW2oTiEFF4yPhzITo4j3k6iAq
6R2jVa2T6G+NzSoTC0VgPSVWcWv+zlSee7OF9uD+GNs56ieYW7gH2cgKq9Q3
yM8BAwIE.kTFmADBLFmD+wZQaFRhnRHvjZf.LDfkPJBIIXYwDEoIAEiucU2m
aNB+WQN20b9IXPJEZSHliSgNJN2Es.3cmq2cteQ3N2l1N8l06TcuD1mef8aY
q6igBkKzIpBm.L+nybHpZ5MSmW1LbZLcwbfUtwSiyGVoDhGdmk0OfhvJCTD5
zRe4M45qjZy5mKVbVemVuzAbm46bgK9Z7dm8BAPrfJvRJN0hzDoNIbgZsbFT
.YD.Ko7sxvyN3LIDv5MLRGPXTnlU5DJBmenXVv00afjM1.Igyp.jX1Le1sZy
iuzcvjIF+I2T0EUJMQbjNisv7jnPjOSmc2SXTN5Stsgh.w7c1c+.RNXzmbSv
UARLbdO6t6.IAL9SrQTSM74rIPyQbLYZc2wO73OitB7Ify1oy8..Ei9LYHnr
7m4L8lNDrXBDTaD85dL7vYsb3kTBegpuhovzMinKgV5T0p3lQRim24vQFeL.
z83Fo48bjO1lqMXMEjEHfPgOFbBpSXdLUVBnPnYybt9.TPwUCTV2dKlBlu.0
U40DH7J8aBV6Z8paY.Oe8aRgLAuCHEPdkNNAjchDEgFssrshMjoAa1EE89t5
njZvKYmi1MmmMJwkWoyEHDOYqzsbcOVqShZMxxXKT1mKwfzYZl05JKNklzBf
Ywh9g+x5WNmKHtiEBdarAEagDJPTQZ11hHZePpKCm.AAgDTHMwQEoUTg4vB+
80csb5XnfFimu9q8X.s6nD9pW42JPMyMQh08X5Db8lHkrPFQgLkmtMivl5WI
qGfFQN.qkw3ISwpN0CxDyFiiLvlat1uNvRL.fk8IiRVYnCHmYPmy9yut85QG
BylLyPUvU4YnPLZ9b.lYL.YBT84f2ZhwrR95ScY8aaep7aShXzU57FM.D7VU
uH40GB3c1CMHGh186cX8VFfUXm1.cgZGoFJnXkbK.ygotYkRlOZKqanv3tvr
gf7pEJ.8nBPexe73y5JRML8io+qh6muPEe.20AKnbXFrrplAQqNXgL1GQjy3
AqydmLa7pjtnFlsLECYrgYHKstDvjUGiLYuvzMF0zVeNWDT9Nklq8QgIu38Q
eLo5m7cAu+z9iwGVqe493MwqVodw4hix2E73dc36TG1i5btPB.20lVKPUsV5
Cv.W5jeMIyrlMYu8X74BSy2Ebb+oncQguu6a6l2JxjjeLDREnHMZBlE6z1TO
dTBNwaWsdYz1Gi5NRPayADX9BENHDHHFHnRjfaGX.noEXZjalunx62.uOvnl
oq1jwFBO+olDl7OvdxDyi5JI4Mt5xJJGShn+mBd6pnPcvzdnhsYqVc7fBLTe
nCZ8uALR90UnE8tramTFdDsCCxqnQixmglgkoYyxPwGWucY7G0wpaBf+1mi2
k7GZj+7vTNSAd+GizgCq9dUq4momr35+kFmJrXCoyCfvgbgdKiXHSsFgMqFw
RT.aalikIHmGlxLuNbySw6We74WJZIcSCKc19KLXDMY11nDGLsVi0uTfDOTt
CS86jeRbMAQpSxBLvji.8IeHG8xFQOKqFsYjfAMXPX4if2Ar.dsXwnj.nx4P
11wcM0GgvwLIP4NiEh4.IjioyARHO4jiCv2ZVHmSmCrM8XlUrtlhx3pRww6W
ZxaMvMtoAquogtwMMP8MM33zzncZF1nIW4pvMYTaE34.8Hick3Ugr4.gtCky
frvGylCTotEo3QVomyMMKL+8HqYA0IEKnwhDic0BBNdNv65R7bfxyyL9aj31
5tIYLVE5jyKn4I3ZOAW6I3ZOAW6I3ZOAW+FOAW6I3ZOAW6I3ZOAW6PK2Sv0d
Btt4Ngmfq8DbsmfqGyNtmfq8Dbcp8eCPITsqr133wTZDD6UISogtdhRKqq+p
fnzv8mmzR6myHdRaFQSZYfyqSZRCBlRZRKCq7zjlmlz70UWec00SSZdZRySS
ZdZRySSZdZRySSZdZRySSZdZRqy3nmlz7zjVC4Gimlz7zj1uhoIMH55nIMjo
RcM2oIMH9poIszd5sjlz921G0URRCNOHIsLwjTRRCy8jjVIW6RLyfvbomkz7
dy06M2gsjS7nQsYMENOTvayYogE5wA0i5AF07kd4BvdTjpP8dWVtTeJj5JNQ
kR5FFUlCiHF14.SesWB28Demm36rCTQ+xt8A+lUvfeqZkXc4mJ36RJRdc2Cm
hN.cEVSFmO7ncGUwkYvCnwzADddymfHOeB1a3LW0EE44Sv9hhEq4mHOeBNLy
tSLXxymfCAeBh77I30O6te.omOAsN6F44SvqZZMxymfW2zYjmOAsgfSOeBxL
wkhx3DOCB5YPv9ATVK7vyZfxyffcCu7LHXO3cIjmAA8LHX2gBOCB5YPvdrxu
mAA8LH3nsRV.xyfftyffHOCBdELHHxyffWICBlKXGrSifngKFFlbRDDaNBpY
QTLzHIBVbTnN0CWG5OtrBnzFq.BWvIRBJcTBNyGIJd7A1oIvgb7XPo7up5tQ
lPtbdA42fnxBTNprFRZaDMlZzpFVVX8QAm6GntRwNuncyJivlCAo3HaYkdcj
pTGwQT1Xpjr5.JR6IOj5Ojoin55W0Le.874xTbL0h5yQahZmocsAk00rMNlx
rSHO6p9FO6p5YWUO6p5YW0QkcUQcjcUuR5gbhocUjIXVlWFBLTztJ5KRZWkC
pZv2WJjvJ5KTRX03cSOIr5Ig0ekPBqTgmEVmFVXkMG3fOpXFTiG77QqmOZ87
Q6a77QqmOZszJDStvsqsLKj83XKAAmCkEIvLn9P4IOWO449FO449EE44hlCL
T9WRT36.zJtd5Z2yivddD1yivddD1yivddD1yivddDdLGa87H7jXGfmGg87H
rmGg87HrmGg87HrmGg87H7ut3Q3ZqH8YMz38GbtVzSHyhZQuf.yUK549RQe1
y6RQVyTTDUp.Mb6fIfwD.eon2WJ58kh9dlv5A5ZRR2KDRWYAIQMWdQZtOCgJ
klpUzID0ByZebRboF0U9qOpV+MTipv5TGV51soVrFUi0qdrcUj0olrryTaSc
YIUlDjopRjxx2lZLAFvJ6ECaZMcPyoKZOaUCpiZQaPSpaZSaQiZqZUaUyZKZ
WaWCaqZYcPSqKZa6hF2Fz51pl2l091rF3l0B2nl35zFaWibMZkcRyrcsyk0f
TVKck2uMs0E0Xq1PvxGJ+t0pzto3N2Ac20o+VYT6BNEnswMKmf0oCIdAkyjL
J3R0TfXsls1PJLTzGnE1Y00hrUSPvR0Ro3.kR02+cOpEFaDgA0ivPvcsiebC
ijYJZ5bqEnr4IDsZe7KV1kU2gIgynDIAjj7WOfz2GqOxjil7RJglTOmsyq1G
8eeJZ6ieRS5mwAurd4ZyGPW.OUW3zAsBwf8gaeJZQ4GPcYtT2PdlCHORjrKT
yjW0dXyn9zaO12h1wJE1ytodDMLpGM0Z3zbAWmAWUzU1tBQxsGLOs0E3j1ft
v1gy6K8etCuRSdBywlpCZVdzIQykkaZAcqr0qtIpBEjgQXExXKT+G6BcgxLb
gHDhERX1Ew0syrF2cVc6PqJ103N0ZZ2ZV9ppaWaMrysl28la6fqocwUYmbNt
atR6nKklOM0wchvLfvsrpX8apywM145l6bZCdcXSdsrQO22rmCa3yoM84zF+
bXyetsAPm1DniaDz0MC10MD1xlBcZigsu4v12fX6aRr0MJ1zlEqeCiMroQm2
3X8adzlBMaq8X8y4jop1MW8hsom1t9XNCT2FeLJ4Z2GDdH3iQa1n+WsIq6Rn
u9iGBTK8r6zQ61s1psqsa+Za1vVucrorAgQCAuN6Xaw.gJFIT0PgodP6QyfV
zxyaeXm505gqVQez0h9DGQeylHfR1BIkxAmsPSY+qPZqLI85bnn7dhuLQZ7G
JPcZnvLOPJ9xA6y1279nvCwaur24wG4gbWmF.QjEHLQvxJnNIwqJQG.zLFJq
xiBDmqLTegLzjtrR9UQNLEiK.WGWTnckwEgXg.yoTRlxJMCBn1.OiBw42fz7
eLx51OsL6oJsw31lQq.7Ltsui12TZciOF5H.po7hxSTXx51IZ0w.77dLnJmP
zW3mNFvuTW95JMefvcG8uky.JWJi5IvJfiAvZVK1d87wNTZSwW956S2rCoZE
.5FLBUo5I0Sc9BWsCJUmhxDHRYSR6xPArkghtYcdmFJp5iiJEoo5Gkrk7oML
VYIYoqjNp42ls0zRsTxwdURYWc6WuFU2Z+Bzrp8S6L9yct8W5CU1cksEsn04
W2qx+KMGMoVbSyWUem7Upe9wCje9AIUbPLIuROue989426meue989426meue
989426meue989426meue989426meue989426meue989426meue9+0te9ssLi
69wucu3uY8gicH+GLImS8ZyZbQkFWPobK0tdqQBkstTg6Y2D4NWwuT9KyU.D
1..1ThC5D3ULKkspnuHlVmBlRHaUhNwlZkFXVCbQcENOl221GtCsOXUEdid6
C0g1G4Fz9RxJnNMBil7VHtisP3T2BMx8N09n2hw3Nhe2h1Gx04vBxMpEBctE
hasEl6MKT5icvPp+50VQCZ2vIaFMkuJiNlD6nNAR6NYBhttpKCBKzLXJAetP
DnqMoRjNQQgnrsBJvY4V9rfqY+1.MY+A6LXgZtT7rZSb3wdhXD0tmUaYlREX
pDyI5Tq0rw5YAhcH7kcGNF+xgOeWmoZcdif1g0OsUGsBVgu5iQh6ZLhGrh6.
pEb2PDxboC0AooAo2E99fPEh7oCWnjUn1gxE7moaxqjtL4dHwZcvzHokvZDt
jVgDxN1v9lMC8zoA5yssrO28xzUyjUakMbNA3MmnS1brHKdyHJcJb1zJo2OJ
tk.uZNt05zcBn9sg95ljaI39HhforphHSBu2NBSeUKerzvIzcUZizNOSOdKK
Yyb.DSVUUICLsScqvhA4OgsUG2EtO7kLFB+9BGRRFytqV3R8MlVad.KT+Uzx
0gaCTai3cmKSOHPPxwPpdS0acX0KEuRszINpyCyxazpgVGhwPtdHFQQ.NPMR
SzCwXiQxXZWM4aTnQ5NvQtiI4zNKHvSBZVfENyPY7wjMJcEKHnQj5jcmNJGS
ZKNAngt1JfSIoX5bSyBRNxjhIz0wNxnRDjtRsqPpmu+778WsCpd99yy2ed99
yy2ed99yy2ei4Xqmu+lD6.778mmu+778mmu+778mmu+lM782W1rI2wfMAOXm
E4PVXQNP8rH28kNulln+MCU8fL7CQ8z+VO5lq1Dq9NbtCgOSKdIGpRzwn8uy
jk94Nwud1YkDht+wYIYqZ0Sa2NW2sJd+KgIeCrgaTN2w43.lPqcP1DnH2W7b
fapuiLYGjrk99v0S+KaBdZe7ocAmiJ.G5vrFkpcoixDIYQlg6opuihl.IZw.
IQa+b3p+L3ZBcRolKwMbJvtfG2cRW8N16LmYhksLQXTvJC0XxnsHI8qOpwjZ
TljR3HvTtxAyGElwDpLnfBuvVDROOY54IyuH3IydFlSvVB0BbRsoAxM7UCM6
2cOPlTlVDs+CgaBVuM3kCAqODrOR6yonkAJTI5VFmSODziHW9Zhvo5zB0yPU
LQiI2rJrUlF6lD2sp0keTq0t6QPeWiI7QGaSKkcT1rAb+1dE8xyGfMUsBwTw
ePIaXDLCBK+9nJfNaTEXrD+KGMAr4i.KgqkQuQJB5WLey6nbY85+RqkJNr5e
cQ4s9oEbPYc4g.k0a5TvPWyILV.bHHy1.qFAft4.o35ARyLyACGC0nVJP1H3
AmpPaU3XjeQGwvOi6XaXLCeQlqwgWwbCdPaCtFKfrwLvZcMR.GyvpsCiEiUa
v0H6E2P.QN9GTga9H4VdbFq6vwYfw05fOsCt6vIZPSKk0Hzs+HMfi2QZ.YvW
kGoARd8GoQVe+UwQZfD8+HMR6nymiz.JmQGoQF5757HMPvo7HMxvJ+QZ3ORC
+QZ3ORC+QZ3ORC+QZ3ORC+QZ3ORC+QZ3ORC+QZ3ORC+QZ3ORC+QZ3ORC+QZL
HYnAjdcYnAULxInwfbvMPxUevMlN5XdtM0501+oMm993eLzYm11P5Ibt0VHq
nn.njx3LfPnqkYI4AplnmPRDUBAlpMA.CAXIjhPRBVVL2iaPxH0Wtb2768uh
7kK1PDcRSJjqvUCNQFEe4hV.7du0681uH7daS6d1NktMDtor8ssfw5xDHlSx
3SOo9HuoKX5B8CCiDPD0llvpZCwSiuFpR9ZCguw5GPQXkAJWpo1SkSuOsZUz
9OGbXmpSrObygmC2E8NsdoCctHaKbwyh263VkkTpl85kzLp7hjTpXEFkurYG
.lTeQ6KvQGPfS.I0Cb.9rC3xpSq8E6XiM1gLtmFhjy7Ysp8.9RmwOlXzmzhH
5pTl.HABjtJyp8fJENSmA2OPTN5SfsAh.37c1buvQNXzmLCjUwQDYdOyty3H
AL1SpQRn1RGFT.YD.atBiIyo6L7gG8oyVfONc1NYt6.nXrAPHmUF.kSrsMU4
Jcqgjq6AgCm0xoORMmejpWWZCEogf6b3TdOF.5dndPtZl1fISJu8.TJmuCwI
V8JKATHzrYRVe.Jn3pAJpRJIOPARJ4+yYf5p77ADdk99.CwU09P334quOzTv
V.oGbGj75PJSNPPqHJQoh1wFxDQrUQQuuiN6nF3RhG.ZXy9jQ0BYvBHXpGzm
NoqlWZSSeJcdwMIpsXACyWf4TIFjQ2LXaJlRSy.vrXU9veY8KmdoyXgf2BVn
LtQBEHpfiE59MhnW0GTVmCb1rpee82JmNFZmw34qCWOFP6NJgu5k8sBTyb6i
XcOFLAWu8QFhGaNiL0tHVOPLhbHVFSXHn9Jy6fLzrwrHCp4ja5qCqDC.VYeh
njUF4.yMjyU+zW2d7FBZqMcxYUzBQmOG83lSqWt3gSqRvoOG7VSvfYwW8p2J
YVaKli5Hgyg5EcAa3pPWrt2lopH0HAEqFG.XtZ+CXMGefHx1oON3scr3hq9y
FCpQuPArG2YVGVNLXuUcF1vdnIE70i.yVruvIDjA+0qb4JGAXCjzuI+9sA4l
DVcdA4pMsrK9PTMJeBdatfOsN8R6OtRmf4Aff2p5JIu9P.e.GYPiodILZgnn
2gQDjkcJLuG0LbVZgQqRZtxFkPiznDaLUfgEUFj.uFlLclYYKLxTUu1nOEZP
0sYYJCjLsiFM4qkyT062ozXsOJL4EuO5iICDeWv6Os+X7g05WtOdS7pUpWrZ
S3wz29w85P9q6FUUW5RC.scTMXQU0RPb0CM0r7gZmSy.L9rf82Ebb+oncQgu
u694qMeaw.IoPGgTAJf34iq8LpdUBNwaWsdYz1Gi5NRPayimHwBENHDHHFHn
RjfaGXR2KHXNP514zq8af2GzKtNGyFhSZPMIzDe43126GZps4MuQUWV43Xx5
EOE71UQg5.v+PYCyVs53AEVn9LYjZd90NnEMZ55V8.iGQCvf7JZ5dEX.Vl9O
KCXeb81kweTmE.IiKu843cI+gd.57f4ECCd+GizwYu9VUq9moLsnk.MMZR57
n4fZLfsgOcALp7vGCMmF9RzYaaxlkIUmGyRswNbySw6We74WJZJ8vNFAFQqo
sLjAkl7TBMuSPc7.4ydh52I+j3UPhTmHWXiCCA8IIqG8ZQSOqUOsYVgAMXvJ
AIjCXA7ZwhQIqxkygT3k6Z9TmDxIiWyvYrPLCxucsGPGsFg9z9bKqpGyxM.Q
5JVnCvl.7X0LbszKvoyf78eLmgfcU3DWcpT79klTzEbiaZv5aZnabSCTeSCN
NMMZmlgMZxUtJbSF0Vgq5+G0UgrHedCJrGfYPENw0gC7X1HPtNCwhL7Hqxy4
lFTL05UPcRsBZjF6ftZ+.GOhRPPWMePNpsBWWDJyzuwoU3pfAZLMkBB6l74X
UFoNun5Msnd475MrFpfREpsC0r2zKkFjSKWG+SIEni28iQaO4Jed+vSqVuYy
4hPyap7IdycYt2HsT0bet2pPwqAIIPnPWgZvPLGkPt2X0Knzjg6J2GL6FITh
DfzebBCwIIE4Fh.goIjWOvxsht7L0bFdxSBHE.h4U4nQ772Z31mRKZI7KUdC
y369XcQ8KqBtr.KKbemNF+z9vkqS8uv4kdtLdMN7y9Ob5wPWGHi1D8RE1kWv
X7D3T.fBg4UDJ.gpxO6GTeAOpQfheCH.SlLllDi+IiGB..Rp9ET39HbJDm77
PRrvT2hPTn5Kq5MpcMZxM+t0a0EQnnyCtXIBxL2ZlDBhKHXnM5k2c43yikMH
J2AwphByhyhvBNfJDlWYsQWVXlQAFrlo+gadk4aaRjkK.mEvCIf.4xjthjR3
bifDRZDtnrJ0yzLQXkdt+0cQaC9ovsGB9onWV+P7lkE8PZ3iOVVtEqGuSvbR
Fjn0iPxDdF0Icu+vl0JC49Oz0cJWm7kq0mDRaE90H2deI7WX4JrUs0Tqc1F3
9b+p5zq56hJAf1FPMyBNOclmNoZZ.mR9424QRkVL0ZPHYdwOsBHBENUM8JGs
83L1NDpNyo.LS4U0W0hBPtPup+8Vek8a8Ukc.Umo1cIhxmBSGjlSF+yYqkBm
vShFUyog9G1bJJePf0bK21pBPNiXVU7bmPpuDbB6D6i+31qpWbosSOqSAOw8
hu+SgWWm.fTlAXTkywXjYNNBBg3oqS7G2GEck8hjQAcEPCarvUa61z0A9Oi1
rI9it2CxWGI+SIURRpD.Dst1qxtatQSJTjTRQU2G8rQkiaG8zKODs+Opr46p
FnzSO.IhaTkIDzjdCLu18QsSr2XPntWzK8tLBigkoSQ3oFOqF.j3Vry5V2wu
zucezqz1F3PPRiVhILfLcaCbX00ymeC6GC20kYl41wyODdLN3GzQT0cCQabJ
pk12tpj8tf3UqNb1jl1KpxBrCUUYazBAEu.hXHALMM00YZMhq2rMVoYDSMYH
SaDCH3WeEHYDhYH9lj+gjhPDfu.I6KPx9BjbOiLciVu.FtG44QuXdnViKQDH
k17XW3H3lyK1gLN0UqgcLd60UBjafkYaHHVakOgJv.Njree6y5i+OaOjDTvG
eN7XfVkzgmiO94figuO4hQA54xAGBUx1QpUYStzGTcq38AJCTB+fN+jx3imC
GNEcXQOhWefC0vaaBaPQVlDoC8WPFSCc6KWE6iTp0WVzpjNT7StxxREDfy30
BtZAWHQvRCQ56CDym5By21CJmSb8k6jTzgyUafhiAbBlXJLp1n7jaVd0DtzX
HPvg0O8gdTinaN4HJS440F28zKSvD7oMoi5YrhiuysIGL9BJGJUyO3HAER0S
Nd8Fu35w6hGPdMQKfXLCiDgqgmFeLiWPtrCAIGbJiLLmaY3oNvvbMPOEvaJc
ccKcwvyq2Edtv.2tKFP7ZcwfRk9V0VLaxQCRS4sEmb3nLrmukJ4NAIwXCMiZ
p.mLyFMnd2I3cmf2cB8yjyMwgK0daHP.FXlh0gsGan2FQpk4ylpZtR24wn8Q
KCB0wFYfpmMbkTCYKVpltFfTe1t.IWYwpjIztWlMiJfFFbY81gCVZqZZfRse
WuUMSw+ZdfDF6CBd7zQ019GN3nsbeEkRROjK7r77.OHGeNHwp0.sa4h1+w38
pq7rZwgb1QM..TqUYYgonAqSgbsSQDSKD0X8d8gfUcmoIHNkS8tUdISU7J.K
nBAVvnXJiPnZmgvmOj+Tl+zVsO5+9Tz1G+T2y3dz05WaywWojaH.IPGTeBrT
PvBcUYY9fTl0qTFln1hQvaMS+HAuUYtqx3vCQmm9E7VkIbwahe5S4mc18JnO
uQXc45jnAOb+mbUPDJXKTZzXbJRhRWtCyvyGlYH7vigKi9b2QplKJok1RXsU
RJi2a0jeBEfQXhHW4yELs9mqmk9hgDGN6tt9VvKrY8xbpvWPZqnqkBDRnNSA
xai303JylM58F3byNlpm23Txt5GrX.MkvF2PAECPbLIietwRff.TWL4J.BDf
wX.Eg3bZ0.JbnSxaWbcLF0RGSvPDFfHIpVOwzM.bHBvEBkrIVjDwppNMASfB
gfinb1n2ybZHi2bOqZnuONsWz.0dmciDVZvML4A9Zpm4pZABvmll9zzzmll9
zzzmll9zzzmll9zzzmll9zzzmll9zzzmll9zzzmll9zzzmll9zz7K7Xnbezi
w6W5bLThGhXnjCMgHXywPI5WewPoRqTxYDYhHBgIysXRrOFJ8wPoOFJ6YJYB
AfdPJ3WY3SRMYnC0vKPTvrgcfz4M3Akj5gOe2vxCeop+u2cZz0PSnlnLYpiv
zlDXHDXejXtVVjWHL7WM+VHwzy7zUN94oKhlvIfXBlBnhzx4.hwtEosaybRV
7tqKOm6tLCwXPE1DvolTSYdnkIZS.nojIE6PjiVafT5LIEQDrbIsSZBpLY7y
c+BGK30JRjlt+3qMRrrFgQSj7iNwG5Eq9QbfBxZMvGEIQxNFXXtJvrITG+sp
Md2c4I.tqjdeMaSn7lsKtIf5TeqypWEXRjIZwgHoHqL8Lixs6kG1o2RQSQQJ
wIVX0Z9ue1ln6O+2UR06AEyssjYl377Ixc2E+9nOmwPdWIylNrvGYAWBYDnN
JXRSDcBmpo1Nj.Q3.N.mHAa3M2oaEkF.y+tOGzS6Vk3NG+uCJZqiubEZinRN
ilvYfD0lmnZNzCPgbJ2nsfMeXX0jLi4yJCb5IhKD2TDOa8MILIVWnLkMCvT5
ZTRmMn70odfxFK0CPiiKmEJD5YD+SFhH9OEGfl0U9RjjKwsSd1jx5tnDVhXA
EJo3esP4kR5Hx8KtSsgiZgToqbSTtO3KqWtKVYwW5vBTZp3h5zlUnsRKkyp0
meORo.lWZ0vpetorNj3bGm1PwaAcaaZVxTjQttxvbs3+LhSbHtxWQBwLfMNG
0Fgy4chbxkTbtoQY2BtoCdqG5Py.ZWNwGJtq8G1j1ej4nNvJSEgZquoo7mtP
pyAdYYs+V9bSoJV264BQ8p+w231FapmT6dSiVOpAGolFvwo0m6D2TxzCMTIo
kOT88gpuOT88gp+7MT88A83vFzieT0rWEu+kOaO7FYVJQjfZCuwymfSCuXy5
CGKbvz28voUqx08J5MyjveqXVVjlKBpWoSZwjomjB4gq8fMx3BNDqLsWfUFa
RAW9Ap+JEz5B4R5vEYolN9Y+2pCWMf8gAjkgAR6CCENJ2Zbva8N2sNXTWtNz
4XaJZoqmYBkF.ImRSA0j7fD1RrqBGxPzU8FA5REzwfkQGMoIrywqq7ZBWWnd
uGk3Vkzf2Ehn9JfZon2EZHTEjozmJLSJ45HUxG8t9n20G8tCcvGhG+fOTouO
wU8xaQzFd3Q0LmO24SsBUHDn1q5loc4uMwJ9.d4Shx1gxq5wI00zzo8bSPk0
7gXgt88cHq19NMcecsF.clfojhYut54zqtmyLocxqtdNoY48uE5PemlFPwna
j7tIWM6dL4Cw8On70V8T9qt2AJg5IUbknZ.ZN1TVbYPcEWgxwbLfXJPdTlCw
dRoFzY6cFvwBETodlcen.dUqGUYOM0JpZHLHbZg12kvSs.ns+kvjuI1sGxjS
ChQLG3N1TktmGHVSrvT2qkoTvsMhx.IgxKVMoVHwH09muTqXgPx7IsDz4T8m
CnpVCf18XKmOK.Y82ilCw3RnThfIfr.LaBD0TLVGZjI+p6v7sM5H07kXcvLD
LeB32viwKen6ZJHrAI1+LxhHEHgTqgiHo.DgMahtb0ma8Kmd4yZy.6NJgGRT
RstCDSkWpLzBw7IJaKxhRCVl1zMHBpDjzV.h6lEfkbtX4SiqFmLVmiFKc61b
3XMNcrdGO1tyGqyAjkNAmVcDYImQ5.cLUqCIcvojt3XxVcNoiNnrAmT5liJa
wYks5vxVcZYKNtrcmW1pCLcvIlt3Hyt3LyFbnYqN0rYGa1ryMa1AmM5jy5bz
ocmcViCOcxom1c7YYMHk0OW48ak7VJprtwx.+4n+1Fwszr6TZZeYsTKWbkKj
ZvNfhAFPgE5Fa.0BaCLfvH2AXrctSZdhb0wsRCH7Qc.9fFNwIE+3udvOm4ho
ADPQN.nNwMS2FHshUrEQzxb2Tytgpwbhp4xOPUWoVqzYW4xoYIv1DWO079Ep
.xnVAYmv0dv8SyRjcv3FptML.4sNLTCOQ0hfdG3JpY43gEtjpiHKsUj0xFiq
8.45L2R0faDFQTsbVn1QkBiAl0.OT0XdoVICLqyBwpYi47EfECiV2LJgcvv1
FsgrZImYDA3JIHb2Lrp.of0hjoqDCViGJUEnr8cF5j.aQWlTIUhqBy1x5CKf
c0rdqRpa1T5aRr9gGbZHpSBX8tmS6POGRcnme8rW1zzwqvpYMMji9RZHmzkg
bd6875Y+roo+fF39yqlQxZ5LtxbZul64cQsUYlUy5JM149nVR0jxGwSu7Dby
odhszOobtKMETq7xS6CyEq+texbHv0xrxlyLhAU6tlKjbII6.vkyGdU9kCGi
SJls8Hn4jCZkrEZ76HEBWn22aluHz30TWVaa.udJZ6mCdalPkdPFLvkqztcj
uLEbgDLJCboF0AQjtEaV+p+.fWdX2B0HaMGBL23LWiuFkl.whR.9y.1eFv9y
.d1dFvKiJEVGcxaOrd3rmFl7VIF59G9Q0jiDEPK+8Nc.cFOCCSzAwRcFLl1U
mcTmtCiE0l.Y+t+uqUFEr+SAl4oesRh4a96+48+71+McnEe1fpu1T1FUsC0y
62AMeD8+sd6i6S3w0feWhE1e24a4xGYUvWa91C9G9cJSb+a+aCd7YcJG70q2
B+lfem5F+l+ZxG9X5GScEyce7+Q+OQaNDEj6a42G.9lfx2Q5K91fu9bS5ax+
k7yG0+VMvBUe3ruojm8e+c2dunudagJ+cG8f9.4qxTwNzEeU1tuxA2drS0aa
C7ZxOjtOgTmWyZnge8GNyj4zPVW1BZq6.cD1ibGZeLG1gbgkJWtVmG8uqL2.
eeteUzq.C59UqFNC4i363OFDs8CQaTFMk+STWTK3VdcQZs5alM6lsfVNRsol
XkGOUa7pI7Ykxxpw.eH20FCq7p.dz68Pq16XbP3GhWuLX0lP0mdYP3wigO99
CCGhgwsE6+F0gL.bACIvTlDmUIMRSgnYAhc3wmeY8QEho1LvSpc+NX.Dhema
4ELgBU+KEhXYU41TgsYA9D9vg3MmNFErMdsxZqUahiGRPh1ZFj.SB6eEjHQJ
SMQbAFkVfnYyHwnzieUMuSYhY7GhB9G+9ADkjskNSLwBSjLvWPofzp7dJskf
mANLSsyXk5mfUEBpL2bvHh4.kab+ny8FYPLAplsxgRBBhELtYI.NOYl7rv2j
Gedezgm+rRVH.R6ACmHmCIlmsTZFhYsywISTBisOVqRz.0AYK5lnaLMEfBRR
H7ZQeVuHJu6s7ei1HAlJRhZU0HAEHnjymaAxkjPchRu2TeJEt+Icb8YFNRD7
WD7Vyvw2xAAuMcLApdq2pGXLk.pNqJBCae7w0wixk1x5KUh8djqvhAXlXZO9
jlVs7CJSJdIJX4ZccMS8gT8ofAb4xQiLaPFK1TFyt.WN2r4fYypsJa15dxrJ
mEJ9gLzBLWvXH0ERK6dSqbaOYZN9.dbdDvWpr.BT3lX.SRprqwWwb+gqztAg
MhEIZ249C4HRjB5XUD4BTTs3uOXA6S8ET9TFzEQvKvHDJstwCSyw77WcDgGr
KvC7l.OlxkCTxKYSnD030MahGZRI8j+ZDgOmJD5Uqh8taPVIPI0xJrT8OJjD
jQt.bcjgltyPKu4Hh.NUP5kMg.il.DfapAfjEBFfouqKArB0j3gUeuQBpn.W
kVfSMmG3bSynrdRYxDhyjXDXp4iAmaZvImZZH7tfZS6.pqMM3jy8GDlqVNMl
zqAwUBHwh7WY9JCllp+pUJ45rL.hH.DNIaKybrR02aJYoF26r75kFv21lFjL
4ygbtogm74PtxNLYaS4lRcYvQk5xfcfJ4FHyC4vDSdfDkoyHIigAmYQZigiU
dmwpy6JA+AGSFHiMK1EJh4LuMIMaXcbZFftQHaigw5iCgTIcDesf.y7dlqFK
QFyoQPXmlFgFMt7Sd0LJ3WH7iSMLNRzxmh9r6b0BpVJFIqfoTjpVpsVRQP7x
7WCAMY7rhNr+O7b7wO6L2pvgsvRMMxYOBSYvVTtKaod4ZohNLL8YSnK6dOlg
uF5jwLJiExxcY5zwlNKi1D9oOGPgH8+4bGGMDcbJtbGWBmrg5CgO693743W4
Z5tPXEQafE9DZP67Q+kkgOVS+DTseRZfMnzjAUk1pQOEM62CWCOKSSxZPeXc
zGSxhn0aVe7S4GlhWsRGG.FFNHQUw2l67auyjIWK2qVd3bV0j0yeby5Ge+wm
2Ge5omye8JIoSw23CUeiGdJi7.NekK6HrQoryqjsL5kX0hyaWtPAD6ByNWt6
zwEQpr1a04uWxBH6Oc34nCKBWuR+mucSb7NswZ5W+g3M4WO2V826JjhYXScN
N4OTZtpYHGzig7cgai13rnJ7bAK2pvpZgRcZmRwo9pWyRYbsSYTZWSKwf5R4
Vyb0U4wTa7+3ckB.chlnzHBkktXBlkDI5Z5SifoBjlIiYl5QgPhkPJihQHgn
v9OqvkjzwGKuXyhs5zRVADiXN.HyB0nWefWxGp3YBavxxlLmhnULUlWvhcmb
NTYOIvLhfDRBI5IXX94HFFIH07dm+xJ51vJxDCa+nv9Uu7nIi+iF2WHTPANC
gfwuePo8rePIbm6GvqpeHk4OK+5bpXAWoc2yqWtLMYfu9mMtsmMe7d1sM9Io
i2yF11ylLNOa2Tgkijw6+iB3ziBNDOJGdRYtK7pdPBtCOINe.5RB1j8jvt7j
fC.5wcA8fnl09qTMBgPIVP3Yg1C2DZJ.SHyxOGZJi7BjEC+hRGbV+e1cAitp
mDykAdzPnJvEY4riy+pdPTWdRTxP7jnt7jXC0SpskKnCwvDk35SBV2zSpY6A
LCgAxM7vTw+hZBnLtgBNR9qqscibYrXHVng5hhAxPnU1EwqgPI.wE8MjA4Iw
tds+XpRZo795Gd8+DhKfBXH.EWzASFhI2DfCStwxllbKXvbSmg.LMINuK9mz
r3EE.Fp42DmrjDTT6UNKlqo2X1rXVyGZpxak9SrfajsfFVPS+mC8dIcr6Apa
CAC6NBwtXLMVL.RjEq4s08jFhkPwbW2fvU+jbQIGdHL0A6zJDCgoNXmzQMXO
ImzQc0OImlyULrqFrYXt8rA1e1WmtFmL4e.vWjSZQFBSxPtnEAMDxLHWzhfG
Bi+PLW2RdsVJkZMj9fPJcpiF+NV4MFZobm5Ckr1afDxQtHkyFBaZQnNLPccO
ImbIwfHl6xCZ.dNPm7Z2PXnAzscdLDOIWVTtjumGMK4ft33.DrIK9442MOhj
2d+z+J0bejIBYGBq8cBCMV6Cb2ZeiVuKV2yJZruoLzHRzYJMajI4uFZUSPhv
oE+GBKDgDWlJSXCxiBd8atmffKFE24BcaGVM25nlc.NFsNWVsZPFjbUevU+j
bZSjPxPbnBPrvYyou9mkSdmeP1gBzosRBGjchCcZyjvAY2jPL45mLJX3EiyQ
+.cZ2tsz7jpkAIPtfvNSpQiQKE47f10K3614FiGjmEv4m00KL5xihMTOIXqV
0LDOIm19izgMOhL0DWnTW9GKE5mv7QO2PYbjSsbxXbpqt+ngCczX41itjyGF
psHfbUpr1sHfj48aN0v7vE+qz8HPDnAaOBPmVzePNSYHzoE8gCxBwtsU0A5Y
Qu9U53T43GNFPmLQNw1r5ESgIapCwLMNB47ecsMtjClo0PnCOJgyk4g21DXg
XDe3sE.eD5H9vaelxH8vcIfCpLG0D.xkHZG8ynDw5ThPcpRjN0SfNkINmjTx
yPnGkB54KU1MMCY9SIblv69wnsmLgBdFGzTDId3oUq2r4bLamOY9xxlfzb+L
Ku7JDe2HIQo6RGD2X0VoLgyMFqKRL4yzTy8.OGT3ThDjjhnDFhSnIuRfvTbR
ZiV51PWdV.nz7D.Rgg0Gw5KAKlt2VCL8pARNVd9ye5X7S6CWtNsvjAJjvg2m
JHoKIhk4Qk6dQAnqytm7BdmGL9gSOF1z.PzlD1un.ppK0dbpIX4gBg4UDJ.g
JFa7GT2bBKVW7tQ.lLYLAx3HClJ..Ho3MWLN84T0VFuOAkwBo4UTHfIKdS5r
sJ4Fe25sZY9nyCNXIBxL2V1nKRseIbwAGmj2NOVTiHmihAEE5DmE0Db.UHLu
pRCrrPGiBLXIS+C27Jy2znHyYOMJj.0lOMYOgjR3bi.ARZDRnryva9J7nRq0
+5tnsA+T31CA+TzKqeHdyxKYDT3iOVVtCqG6RvSRV2VOWljm4Et5IDu+vl0J
MZ+GZ0cMMwnbscnvuFn1xKg+B6LUDYuYTqDeckahhickZ5fbNf1x.fQp77TI
dp.9v1gyUKMaE4UZFT5kQx7hB5I1DJbnaVuDc3P3SQC8XQeU2jSwQ1D+pupA
EGbgdEs6s9pp21rbMthyNbaTLeIH0Aoqjwrb1Cn5y3AUaiIYg+CaNEkQ7R1a
U1zFB4LhQi+4FnTeI3Hz.2G+wsctEdocQOO2DORsvu+SgcuABPpkqLpx3XLx
L2Ao1dId3af+w8QQ8nElfdX8OFKlz1IL7Mt+ynMah+Xyst7Et4+TRoalJA.Q
iqWnrQiazr.EvD8eTEhSGzNvoWdHZ+eTY6PmAWsnHHY3mpVRilzRg40lMHMv
8FCJzsPm06vHLFVlJNxSMnRAbRbCqkOUcnK8mlQ7RlHxgfjFjDSX.YpIhbXw
0atcCSGC20lzeNKW+gviwA+flJPuqOOeyFysPmr50y9p+mu5+OGd+REC
-----------end_max5_patcher-----------

groma · May 30, 2019, 5:21pm

At this point I think it is worth mentioning we are focusing on design rather than optimization, which is a common practice in software development, so hopefully there will be further optimizations once we freeze the design. I can’t load your patch as it is missing bufview.js, but if your focus is performance I think I would try to analyze each descriptor object differently, and they are definitely not direcltly comparable to framelib.
I do appreciate your effort playing with the descriptor objects, I don’t see them as newschool with respect to framelib performance-wise, but they do have a different interface.