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Musical Genre Classification

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  1. Musical Genre Classification Prepared by Elliot Sinyor for MUMT 611 March 3, 2005

  2. Table of Contents • What is Genre? • Approaches to Genre Classification • Manual • Automatic • Related Work • Soltau 1998 • Tzanetakis & Cook • prescriptive approach • Pachet et al. 2001 • emergent approach • Conclustion

  3. What is Genre? • A way of describing what an item shares with other items as well as what differentiates it from other items • From Aucouturier and Pachet • “The genesis of genre is therefore to be found in our natural and irrepressible tendency to classify”

  4. What is Genre? • A&P separate into two broad categories • Intentional vs. Extensional

  5. What is Genre? - Intentional • More subjective • Relies on collective cultural knowledge • Social/Historical context • Eg 60s, hippies, brit-pop

  6. Problems with “Genre” • What do the names mean? • Rock? Pop? • No fixed semantics • Amazon.com Genres by: • Period (“60s pop”) • Topic (“love song”) • Country of Origin (“Japanese music”) • Genre is based on extrinsic habits rather than intrinsic properties • To a French person – C. Aznavour – Variety • To an English person – C. Aznavour – French

  7. What is Genre? - Extensional • Analysis-based • Describes the music itself • Tempo, timbre, pitch, language, etc. • (sometimes) easier for automatic genre classification systems • Eg fast rock, mellow classical.

  8. Problems with “Genre” • What granularity to use? • By Artist? • Please Please Me vs. Sgt. Pepper • By Album? • Revolution 9 vs. Helter Skelter vs. Mother Nature’s Son • Does work for broad categories • Rock vs. Classical

  9. Problems with “Genre” • Does anyone agree? • Allmusic.com – 531 genres • Amazon.com – 719 genres • Mp3.com – 430 genres • Only 70 words common to the three taxonomies (Pachet and Cazaly 2000)

  10. Approaches to Genre Classification • Manual • Musicologists and Elbow Grease • Automatic • Prescriptive • Signal Analysis based • Emergent • Uses existing human-entered meta-data to group things together

  11. Manual Classification • Dannenberg et al. 2001: • To build a taxonomy for MSN Music Search Engine • “Few hundred thousand songs” • Hired full-time musicologists • Took 30 human years • “The details of the taxonomy and the design methodology are, however, not available”

  12. Manual Classification • Pachet and Cazaly 2001 (CUIDADO) • Separated descriptors – country, instrumentation, artist type, etc • _____ Rock • Too sensitive to musical evolution, difficult to build, difficult to maintain • Changed focus to artists instead of titles. • In any case, insufficient for millions of titles

  13. Prescriptive – History • Originated from Speech Recognition work • Most Classified audio from TV into music/speech/environmental

  14. Prescriptive – Various Approches • Saunders 1996 • Thresholding/ZCR techniques • Scheirer and Slaney 1997 • Multiple features and statistical pattern recognition • Kimber and Wilcox 1996 • MFCCs and HMM to classify into music, speech, laughter and nonspeech • Zhang and Kuo 2001 • Rule-based system for classifying audio from movies and TV into: • Non-music • Pure speech, non harmonic environmental sound • Music • Harmonic environmental sound, pure music, song, speech with music, environmental sound with music

  15. Prescriptive • Soltau et al 1998 – “Recognition of Music Types” • New approach – Explicit Time Modelling with Neural Network (ETM-NN)

  16. Prescriptive – Soltau et al. 1998 • In a nutshell: • Transform acoustic signal into sequence of abstract sonic events • Look at statistical patterns derived from sequences  combine into vectors that represent temporal structure • 3-layer feed-forward network

  17. Prescriptive – Soltau et al. 1998 • Experimental Results: • 3 hours of data (360 samples, 30 sec each) • Rock, Pop, Techno, Classical • 67% training, 13% cross-validation, 20% evaluation • Compare ETM-NN vs. HMM, using cepstral coefficients • ETM-NN: 86.1% HMM: 79.2%

  18. “Musical Genre Classification of Audio Signals” – Tzanetakis and Cook, 2002 • Timbral Texture Features • Spectral {Centroid, Rolloff, Flux}, ZCR, MFCC (5 coefficients) • Analysis Window – features should be stable – 23 ms • Texture Window – “minimum amount of time to identify a 'texture’” 43 analysis windows, 1 sec. • “Memory of the past” • Statistics (means, variances) of features over the texture window

  19. “Musical Genre Classification of Audio Signals” – Tzanetakis and Cook, 2002 • Timbral Texture Features • Spectral {Centroid, Rolloff, Flux}, ZCR, MFCC (5 coefficients) • Analysis Window – features should be stable – 23 ms • Texture Window – “minimum amount of time to identify a 'texture’” 43 analysis windows, 1 sec. • “Memory of the past”

  20. Timbral Texture Feature Vector • Statistics (means, variances) of features over the texture window • 19 dimensions • (m, v) of SC, SF, SR, ZCR, 5 MFCC • “low energy feature” fraction of analysis windows over texture window that have less than average RMS energy • Eg vocal music will have more silences

  21. Rhythmic Content – “Beat Histogram” • “Pitch detection with larger periods” • Use DWT to divide signal into frequency bands

  22. Rhythmic Content – “Beat Histogram”

  23. Features taken from BH • A0, A1: relative amplitude (divided by the sum of amplitudes) of the first, and second histogram peak; • RA: ratio of the amplitude of the second peak divided by the amplitude of the first peak; • P1, P2: period of the first, second peak in bpm; • SUM: overall sum of the histogram (indication of beat strength).

  24. Pitch Content Features • Used enhanced Autocorrelation function to create folded (1 octave) and unfolded (all notes) pitch histograms • Mapped to MIDI note numbers • Folded- common pitch classes • Unfolded – pitch range • Higher for jazz, classical • FA0, UP0, UP1, IPO1 (interval between 2 highest peaks), SUM

  25. Experimental Results • Used GMM classifiers with diagonal covariance matrices

  26. Experimental Results

  27. Prescriptive – Some Results: (from A&P) • Gaussian and Gaussian Mixture Models, used in 48% of successful classification in Ermolinskiy et al.(2001) using 100 songs for each class in the training phase. This result has to be taken with care since the system uses only pitch information. • Tzanetakis et al. (2001) achieves a rather disappointing 57%, but also reports 75% in Tzanetakis and Cook (2000a) using 50 songs per class. • 90% in Lambrou and Sandler (1998) and 75% in Deshpande et al. (2001) on a very small training and test set, which may not be representative. • Pye (2000) reports 90% on a total set of 175 songs. • Soltau (1998) reports 80% with HMM, 86% with NN, with a database of 360 songs.

  28. Emergent • Unlike Prescriptive, it is unsupervised • Based on “cultural similarity from text documents” • Possible to extract similarities that are not possible to extract from the audio signal

  29. Emergent – Collaborative Filtering • Shardanand & Maes 1995, Pestoni et al. 2001 • There are patterns in tastes • Have users rate their music, match like-tasted users, recommend unknown items to users • Problems • Good for naïve profiles, bad for broad, eclectic tastes • Favors “middle of the road” – liked by large proportion • Only works some time after release of new music

  30. Emergent – co-concurrent analysis • Pachet et al. 2001 • Looks at online text sources for co-occurrences of songs (aka data mining) • If 2 items appear in the same context (or share a common neighbour), this is evidence of some sort of similarity

  31. Co-occurrence • Pachet et al. 2001 “Musical Data Mining for Electronic Music Distribution” • Sources used • Track listing databases (CDDB) • Mostly look at compilations of similar artists • Radio Show playlists • Specialty programs better than daily commercial radio • Lists made by experts

  32. Co-occurrence • Build a matrix where: • Value of entry (i, j) corresponds to number of times title i co-occurs with title j • What about indirect co-occurrence? • Eg Eleanor Rigby/Good Vibrations, Good Vibrations/God Only Knows  Eleanor Rigby God Only Knows • Correlation measure, using co-variance matrices of each title

  33. Experimental Results • Using distance functions, use Ascendant Hierarchical Clustering • Used CDDB database, compared co-occurrence vs correlation • Manually examined results • “70% of clusters had interesting similarities”

  34. Experimental Results

  35. Challenges • Name format is not strictly enforced • The Beatles; Beatles, The; Beatles • Difficult to characterize the nature of the similarities • Cover songs can sound nothing alike

  36. Conclusions and Future directions • “It seems that samples of Techno and Classical are easy to discriminate … Rock and Pop seems to be more difficult” – Soltau et al 1998 • Manual classification not feasible • Why not combine prescriptive/emergent techniques?