Finding a single voice in music Christine Smit April 26, 2007.
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Transcript of Finding a single voice in music Christine Smit April 26, 2007.
Finding a single voice in Finding a single voice in musicmusic
Christine SmitChristine Smit
April 26, 2007April 26, 2007
OutlineOutline
IntroductionIntroduction Classification Strategies: Classification Strategies:
Counting silent frequency binsCounting silent frequency bins Pitch cancellationPitch cancellation MFCCsMFCCs
Trading recall for precisionTrading recall for precision What worked and what didn’tWhat worked and what didn’t
What is a ‘single voice’?What is a ‘single voice’?
a a singlesingle note sounding at a note sounding at a timetime
Why do this?Why do this?
single voice finder + instrument single voice finder + instrument identifier identifier
==instrument sample libraryinstrument sample library
What are the data sets?What are the data sets?
training set: 10 1-minute samplestraining set: 10 1-minute samples test set: 10 1-minute test samplestest set: 10 1-minute test samples 25% single voice, 75% 25% single voice, 75%
multi-voice/silencemulti-voice/silence mixture of classical and folk musicmixture of classical and folk music
Strategy #1: Silence Strategy #1: Silence detectiondetection
findsilence
silent
HMM?
music
silencecounts
rawclassification
Nothing really workedNothing really worked
Strategy #2: Pitch Strategy #2: Pitch CancellationCancellation
music
filteredmusic
rawclassification
finalclassification
filterpitch
singlevoice?
HMM
Quick reminderQuick reminder
PrecisionPrecision = out of the stuff we got, how = out of the stuff we got, how much of it was right? much of it was right?
Are google’s results relevant?
RecallRecall = out of all the right stuff, how = out of all the right stuff, how much did we get?much did we get?
If I asked google for the UN, did I get all the UN’s websites?
Precision is importantPrecision is important
If I have a large enough database, I If I have a large enough database, I can afford to have relatively low can afford to have relatively low recall. But I want high precision so recall. But I want high precision so what I do get is what I want.what I do get is what I want.
Strategy #2: Pitch Strategy #2: Pitch CancellationCancellation
music
filteredmusic
rawclassification
finalclassification
filterpitch
singlevoice?
HMM
Strategy #1: Silence Strategy #1: Silence detection detection (just for comparison)(just for comparison)
ConclusionConclusion
Silence detection really didn’t work Silence detection really didn’t work out.out.
MFCCs + GMM is really just as good MFCCs + GMM is really just as good as pitch cancellationas pitch cancellation
At 90% precision, I get about 25% At 90% precision, I get about 25% recall.recall.
AcknowledgementsAcknowledgements
Much thanks to Professor Ellis Much thanks to Professor Ellis for his assistance on this for his assistance on this
project.project.