0.5 setgray0 0.5 setgray1 Music and Machine Learning Using Machine Learning for the Classification of Indian Music: Experiments and Prospects Paritosh K. Pandya School of Technology and Computer Science Tata Institute of Fundamental Research email: [email protected]http://www.tcs.tifr.res.in/∼pandya SNDT 2006 – p.
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Music and Machine LearningUsing Machine Learning for the Classification of
Classification of MusicRaag RecognitionClassification of Music in Thaats and JatisClassification of Raags into Time Cycle, SeasonalCycles,Classification of music by gharanasPerformer recognitionIdentification of Raag LakshansAssociation of Bhaav with musical performance
[B.Chaitanya Deva, 1981]
Beat tracking and taal recognition
Identification of musical structureSNDT 2006 – p. 21
Associated Applications
Music Visualisation
Musical query processing from large annotated musicaldatabases.
Automatic music composition
Automatic accompaniment
Pursuit: Distance Education of Indian Music
SNDT 2006 – p. 22
Machine Recognition of Raags
Raga performance as sequence of notes.Stop Sa Re Ga Pa Ga Re Sa Stop Dha Sa Re Ga
Sequential pattern classification problem
Data is not unordered set of samples.
Data elements occur in an order: spatial or temporal.
The probability of next data element crucially dependson the order of occurrence of preceeding elements.
Hidden Markov Models (HMM) are widely used.
SNDT 2006 – p. 23
Finite state automaton for Raag
Bhoopali
2Ga
3Re
4Pa
5Sa
6Dha
7Stop System can be in one of
finite number of states
Current state depends onthe past and current inputseen
Current state and next in-put determines the possiblenext states
Experiments: Manual con-struction of raag automatabased on Bhatkhande Books[Sahasrabuddhe]
SNDT 2006 – p. 24
Hidden Markov Model of a Raag
Bhoopali
2Ga 1.00
0.09
3Re 1.00
0.45
4Pa 1.00
0.235
Sa 0.99
0.06
0.51
0.41
0.45
0.076
Dha 1.00
0.41
0.38
0.07
0.32
0.62
0.30
7Stop 1.00
0.46
0.05
0.18
0.25
0.06
Probability ofseeing a note inthe given state.
Probability ofmoving from onestate to another
An HMM model canbe learnt from train-ing data
Analysis Given anHMM and a note se-quence, compute itsprobability of occur-rence.
SNDT 2006 – p. 25
Raag Recognition using HMM
Hidden Markov Model for a raag
Finite state automata
Probability of “seeing a note” in each state.
Probability of transition between states.
HMM model can be learnt from a set of training data
Given a note seqeuence we can compute its probabilitywithin given Raag HMM model.
SNDT 2006 – p. 26
Kansen: A raga recognition system
An Experiment at TIFR using a Toolkit HTK:
Learns HMM for each raag from training data(Baum-Welch Algorithm)
Training data: (Bhatkhande, IITK) collection of midi filesof raags played on keyboard. We use 29 raag database.
Test data: sequence of notes
Output: probability of the sequence being in each raag.
Preliminary Results
About 86 percent success on 29 raag recognition
Confusion between close raags
Insufficiency of dat a significant reason
(Joint work with Bhaumik Choksi and K. Samudravijaya)SNDT 2006 – p. 27
Each midi file created by playing from Keyboard (e.g.Des.mid)
Basic database of 29 Raags (above)
Full database of 300+ Raags
SNDT 2006 – p. 28
Demonstration
Input Stop Sa Sa DhaKo Sa GaKo Re Stop Sa Ga Ga MaGaKo Re GaKo Re Sa Ni DhaKo Ni Sa Re Sa Ni SaOutput Log of probability of being in a raagI=1 t=0.02 W=ChandraKauns v=1
I=2 t=0.02 W=ChhayaNut v=1
I=3 t=0.02 W=Hamir v=1
I=4 t=0.02 W=Pahadi v=1
I=5 t=0.02 W=MiyankiMalhar v=1
I=6 t=0.02 W=Adana v=1
I=8 t=0.02 W=Peelu v=1
J=0 S=0 E=1 a=-164.11 l=0.000
J=1 S=0 E=2 a=-163.60 l=0.000
J=2 S=0 E=3 a=-160.92 l=0.000
J=3 S=0 E=4 a=-160.72 l=0.000
J=4 S=0 E=5 a=-158.36 l=0.000
J=5 S=0 E=6 a=-117.88 l=0.000
J=13 S=0 E=8 a=-88.55 l=0.000
Summary Peelu (-88) Adana (-117) MiyankiMalhar (-158)SNDT 2006 – p. 29
Demonstration (cont)
Stop NiKo Dha Ni Sa NiKo Pa Ma Pa GaKo Ma Re SaBahar (-20.5) MiyankiMalhar (-23) Adana (-53)
Stop Ma Pa NiKo Dha Ni Ni Sa Stop Ni Sa Re GaKoGaKo Ma Re SaMiyankiMalhar (-52) Bahar (-66) Adana (-75)
Stop Ma Pa Ni Ni Sa Ni Sa Sa Stop Pa Ni Sa Re NiKoDha Pa Stop Pa Dha Ma Ga Re Stop Ga Re Ni SaDes (-127) Gaud (-139) MiyankiMalhar (-144)
Stop Ga Ma DhaKo DhaKo Pa Stop Ma Pa GaKo MaReKo Sa Stop Ga Ma Pa DhaKo Ni Sa DhaKo PaBasant Mukhari (-105) Peelu (-106) Jogiya (-130)
SNDT 2006 – p. 30
Demonstration (cont)
Stop Ni Sa GaKo ReKo Sa Stop Ni Sa GaKo MaTiv PaStop GaKo MaTiv Pa Ni Sa DhaKo Pa MaTiv GaKoReKo SaMultani (-76) Todi (-105) ChandraKauns (-169)
Stop DhaKo Ni Sa ReKo GaKo Stop ReKo GaKo ReKoSa Stop Sa ReKo GaKo MaTiv ReKo GaKo ReKo SaTodi (-58) Bhoopali Todi (-83) Multani (-124)
SNDT 2006 – p. 31
Conclusions
Computer analysis and machine learning providesinteresting new method of analysing music. It allowsmany intuitive and qualitative observations to be madeobjective, precise and quantitative.
Research with computational techniques lead to directapplications in music technology.
Intelligent music analysis is almost untried for IndianMusic.
Work requires collaboration between musicologists,computer scientists and electrical engineers.
Music researchers must help by building corpuses andannotated datasets for future machine analysis.