Music and Machine Learning

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Music and Machine Learning

  1. 1. 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: pandya@tifr.res.in http://www.tcs.tifr.res.in/∼pandya SNDT 2006 – p.
  2. 2. Outline Motivation (Example) Introduction to Machine Learning Intelligent Music Processing Examples Indian Music: Some questions Automatic Raag Recognition System SNDT 2006 – p.
  3. 3. Computers and Information Processing Evolution of computers Scientific calculations: e.g. planetary orbits Data processing: e.g. inventory Multimedia Rich Text, Graphics, Pictures, Animations, Video, Sound and Music. Computers can store, edit, process and display all of these!! Internet and World-wide Web: SNDT 2006 – p.
  4. 4. The Classical Music Archives SNDT 2006 – p.
  5. 5. Computers and Arts Computers and networks are increasingly used for storing, processing (editing, cataloguing, searching), and disseminating artistic content. Web Portals with artistic, educational and research-oriented content are becoming available e.g. complete works of Shakespeare Computers can be used to analyse artistic content in new and sophisticated manners. Computer as a tool for research in humanities and arts: Example Discovery channel news (2003): Computers to reveal Shakespeare SNDT 2006 – p.
  6. 6. Learning Machines Traditionally, computers are calculating devices. How to calculate must be fully pre-programmed. People observe patterns in nature, they discover rules and they learn. Can Computers learn? A question addressed by artificial intelligence. Learning System A system capable of the autonomous acquisition and integration of knowledge. SNDT 2006 – p.
  7. 7. How do systems learn? Supervised Learning: Learn from examples. Training Example Set (annotated) Feature Selection (Input) Target function representation Statistical learning and classification X y = Number of occurrences of Ma after <Ga,MaTiv,Pa> x = Number of oc- currences of Re after <Ga,MaTive,Pa> y SNDT 2006 – p.
  8. 8. Neural Nets Learning functions Given monthly icecream sales and average temperature for last 10 years, predict icecream sales this summer. SNDT 2006 – p.
  9. 9. Why use learning system? The relationship between data elements is not formalized. Only examples are available. Relationship between data items is buried within large amount of data. Data mining: using historical data to discover relationships and using this to improve future performance. SNDT 2006 – p.
  10. 10. Applications of Machine Learning Speech recognition Image recognition (Face recognition) Identifying Genes Predicting Drug Activity Cataloguing Faint Objects in Astronomical Data Detecting Credit Card Frauds Predicting Medical Outcomes from Historic Data Detecting Hacking and Intrusion from Network Load Computational Linguistics SNDT 2006 – p. 1
  11. 11. Music Performance Visualisation Performance worm [Widmer, Vienna] Different players have different ways of building tension or expression in the music Measure subtle changes in beat level tempo versus loudness for each note played. Represent this visually in tempo-loudness space as a trajectory called "performance worm". SNDT 2006 – p. 1
  12. 12. Our Experiments Real-time melody tracker (Click) 300 sa ni dha Pitch (Hz) pa ma ga re sa 120 0 15 Time (s) SNDT 2006 – p. 1
  13. 13. Recognition of Concert Pianists Characterisation of Personal Expression Features Classification Classification between 22 piano players Classification based on performance worm like data Achieved accuracy comparable with human listeners. [Saunders et al (2005)] SNDT 2006 – p. 1
  14. 14. Islands of Music Intelligent structuring and exploration of digital music collections [Pampalk et al (2004)] Grouping of Music by Similarity Genre and Style Performer Timbral and rythmic con- tent Automatic classification of music by Genre: Classical, Country, Disco, HipHop, Jazz and Rock [Pye 2000] About 90% success on 176 songs SNDT 2006 – p. 1
  15. 15. Music Structure Analysis Structure in Music Composition repetition, transposition, call and response, rythmic patterns and harmoic sequences shape of a song e.g. AABA Automatic structure analysis attempts to discover such structure [Danenberg,CMU] Beat tracking and Tempo Detection Identifying time signatures and tempo Marking beat positions within music [Simon Dixon] SNDT 2006 – p. 1
  16. 16. Music representation: Audio Audio Waveform Santur playing(Click) Rich set of features: Pitch, Amplitude, Spectra SNDT 2006 – p. 1
  17. 17. Structured Music Representation Example: Musical Score Notation SNDT 2006 – p. 1
  18. 18. Computers and Music Notation MIDI files: computer representation of musical score. Can be recorded from keyboards etc. Synthesizers: MIDI → Sound Issue Expressive Music Representation (a hot topic for research!) Music Notation for Indian Music Bhatkhande or Paluskar systems Not used by professional musicians Lacks structures SNDT 2006 – p. 1
  19. 19. Swarupa: Structured Music define kaida2 as pat( 8.pat(dha,te,te,dha | te,te,dha,dha | te,te,dha,ge | ti,na,ke,na), 8.pat(ta,te,te,ta | te,te,ta,ta | te,te,dha,ge | dhin,na,ge,na) ); define palta1 as pat( 8.pat(dha,te,te,dha,te,te,dha,dha | dha,te,te,dha,te,te,dha,dha), 8.pat(dha,te,te,dha,te,te,dha,dha | te,te,dha,ge,dhin,na,ge,na), 8.pat(ta,te,te,ta,te,te,ta,ta | ta,te,te,ta,te,te,ta,ta), 8.pat(dha,te,te,dha,te,te,dha,dha | te,te,dha,ge,dhin,na,ge,na) ); SNDT 2006 – p. 1
  20. 20. Swarupa: Structured Music define kaida2n as [ A::[ 2:dhatita::[dha,te,te], dhadha::[dha,dha] tite::[te, te], dha,ge | tin,na,ke,na ]; B::[ A.1{khali} | C::[A.2 | A.3{bhari}] ] ]; define palta1 as [ 3:[A.1 |] C; 3:[B.1 |] C; ]; define palta2 as [ A.1 | D::4:5%[te,te] ; B ; B.1 | D ; B ]; Synthesis Swarupa → Audio (See MuM Webpage) Music transcription: Audio → Score SNDT 2006 – p. 2
  21. 21. Indian Music Research using AI Some topics Classification of Music Raag Recognition Classification of Music in Thaats and Jatis Classification of Raags into Time Cycle, Seasonal Cycles, Classification of music by gharanas Performer recognition Identification of Raag Lakshans Association of Bhaav with musical performance [B.Chaitanya Deva, 1981] Beat tracking and taal recognition Identification of musical structure SNDT 2006 – p. 2
  22. 22. Associated Applications Music Visualisation Musical query processing from large annotated musical databases. Automatic music composition Automatic accompaniment Pursuit: Distance Education of Indian Music SNDT 2006 – p. 2
  23. 23. 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 depends on the order of occurrence of preceeding elements. Hidden Markov Models (HMM) are widely used. SNDT 2006 – p. 2
  24. 24. Finite state automaton for Raag 7 Stop System can be in one of finite number of states 2 Ga Current state depends on the past and current input 3 seen Re Current state and next in- 5 put determines the possible Sa next states 6 Dha Experiments: Manual con- struction of raag automata 4 based on Bhatkhande Books Pa Bhoopali [Sahasrabuddhe] SNDT 2006 – p. 2
  25. 25. Hidden Markov Model of a Raag Probability of 7 Stop 1.00 seeing a note in 0.46 the given state. 2 Ga 1.00 0.09 0.05 Probability of 0.45 0.51 0.25 moving from one 0.06 3 0.06 state to another Re 1.00 0.41 0.38 0.18 An HMM model can 0.23 0.45 5 0.07 be learnt from train- Sa 0.99 ing data 0.32 0.30 6 0.07 Dha 1.00 Analysis Given an 0.62 0.41 HMM and a note se- 4 quence, compute its Pa 1.00 Bhoopali probability of occur- rence. SNDT 2006 – p. 2
  26. 26. 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 probability within given Raag HMM model. SNDT 2006 – p. 2
  27. 27. 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 files of 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. 2
  28. 28. Bhatkhande MIDI File Database of Indian Raags (IIT, Kanpur) Adana, AheerBhairav, AlhiyaBilawal, Bageshri, Bahar, Basant, BasantMukhari, Behag, Bhoopali, BhoopaliTodi, ChandraKauns, ChhayaNut, Des, Durga, Gaud, Hamir, JataShwari, JaunaPuri Jogiya, Lalit, Malkauns, MiyankiMalhar, Multani, Pahadi, Peelu Sohini, TilakKamode, Tilang, Todi 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. 2
  29. 29. Demonstration Input Stop Sa Sa DhaKo Sa GaKo Re Stop Sa Ga Ga Ma GaKo Re GaKo Re Sa Ni DhaKo Ni Sa Re Sa Ni Sa Output Log of probability of being in a raag I=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. 2
  30. 30. Demonstration (cont) Stop NiKo Dha Ni Sa NiKo Pa Ma Pa GaKo Ma Re Sa Bahar (-20.5) MiyankiMalhar (-23) Adana (-53) Stop Ma Pa NiKo Dha Ni Ni Sa Stop Ni Sa Re GaKo GaKo Ma Re Sa MiyankiMalhar (-52) Bahar (-66) Adana (-75) Stop Ma Pa Ni Ni Sa Ni Sa Sa Stop Pa Ni Sa Re NiKo Dha Pa Stop Pa Dha Ma Ga Re Stop Ga Re Ni Sa Des (-127) Gaud (-139) MiyankiMalhar (-144) Stop Ga Ma DhaKo DhaKo Pa Stop Ma Pa GaKo Ma ReKo Sa Stop Ga Ma Pa DhaKo Ni Sa DhaKo Pa Basant Mukhari (-105) Peelu (-106) Jogiya (-130) SNDT 2006 – p. 3
  31. 31. Demonstration (cont) Stop Ni Sa GaKo ReKo Sa Stop Ni Sa GaKo MaTiv Pa Stop GaKo MaTiv Pa Ni Sa DhaKo Pa MaTiv GaKo ReKo Sa Multani (-76) Todi (-105) ChandraKauns (-169) Stop DhaKo Ni Sa ReKo GaKo Stop ReKo GaKo ReKo Sa Stop Sa ReKo GaKo MaTiv ReKo GaKo ReKo Sa Todi (-58) Bhoopali Todi (-83) Multani (-124) SNDT 2006 – p. 3
  32. 32. Conclusions Computer analysis and machine learning provides interesting new method of analysing music. It allows many intuitive and qualitative observations to be made objective, precise and quantitative. Research with computational techniques lead to direct applications in music technology. Intelligent music analysis is almost untried for Indian Music. Work requires collaboration between musicologists, computer scientists and electrical engineers. Music researchers must help by building corpuses and annotated datasets for future machine analysis. SNDT 2006 – p. 3

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