The document discusses using machine learning techniques to classify music based on audio features extracted from MP3 files. It outlines a multi-step process: 1) Extracting rhythm and timbre features from MP3 files. 2) Storing the features in HBase for distributed processing. 3) Training models on the features using an algorithm like gradient descent. 4) Classifying new music based on the trained models and displaying the results. The goal is to teach machines to classify music by genre based on audio characteristics.

1. twitter.com/tudorcristea

3. Pies, pipes, or any
combination thereof are not
involved.

4. Refers to the story of the
Pied Piper of Hamelin: follow
the music.

5. Root problem: music
information retrieval.
ID3 information
(CDDB/Gracenote) is usually
flawed.

6. Everybody likes music.
What kind of music?

7. Difficult to say? That is
because it is a difficult (as in
subjective) question.

8. Usually neither we or the
artists we listen to limit our
range to just one music
genre.

9. We like stuff that sounds like
the stuff we like.

10. Tough problem: teach that to
a machine.

11. Tough, but not impossible:
teach it what sounds like
means.

12. Luckily, music…

13. …has hues.

15. First of all, we’re going to
need some (legal) teaching
material:
ccmixter.org/query-api

16. Second, a way to extract
some meaningful features
out of those MP3 files:
ifs.tuwien.ac.at/mir/audiofeat
ureextraction.html

17. Rhythm Histograms Rhythm Patterns

18. 3.243435282592375 1.994601142999418 1.602942870377543 2.580839981133314 1.543740353999544 2.068105015908046 3.632687882087414
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2.390710363180224 3.76382419342901 5.030486764491426 4.300433534348324 4.821028562923828 4.280883335826844 2.190830721855284
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13.695306246773496 16.741151349256675 11.890804211283207 13.184462990253897 12.343617842695329 15.58978678059538
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16.716924329462138 25.499979343490466 20.730583684244632 25.659206437972436 22.267453929132937 31.362526063314156
30.022068712065376 35.37839821990554 28.467015190336838 23.240537780206445 27.036056141332217 22.285161424867912 41.07112692673061
59.47331283268421 78.34108682145228 98.71614105494565 8.977171134544522 6.904445013840824 5.079352087117484 6.767011796959547
4.529028036594085 6.974298791708502 8.923714823963904 8.243909316285263 10.317986757880721 8.695841266592888 10.717309150680364
12.49861281405036 10.461037143501649 8.995606768277778 9.136664427796598 12.38754496496926 10.635964588452383 8.69771967108946
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4.96446501860343 6.604293070535694 5.817617856710792 5.595589306370536 10.169040335952701 7.656965890729936…
Sounds good?

19. Lastly, a way to
distributively store, process,
and interpret the results:
hbase.apache.org
hadoop.apache.org
mahout.apache.org

20. Step 1
offset = 0 offset = 10 offset = 20
Mapper Mapper Mapper
for index in offset .. limit do
download(index)
end for
pp/downloads/$tag

21. Step 2
index = 0 index = 10 index = 20
Mapper Mapper Mapper
for mp3 in index .. limit do
extract(mp3)
end for
features table

22. Rows and columns in HBase
(Table, RowKey, Family, Column, Timestamp) → Value

23. A time-oriented view
into parts of a row

24. $tag + $url = { <- row key
data = { <- column family
ssd = …, <- column qualifier
rp = …,
rh = …,
artist = …,
title = …
}
}

25. Step 3
???
for example in features[$tag] do
train(example)
end for
tag = { data = { sgd = … } } models table

26. Gradient descent visualization

27. Gradient descent algorithm

28. Step 4 Upload a file
Profit!
Extract features
for model in models do
classify(example, model)
end for
Display results