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Genre Classification

Genre Classification. Andrew Horwitz MUMT621 – 3/12/2014. Genre Classification. Grove’s definition: “A class, type or category, sanctioned by convention .” Automatic genre classification is the act of m apping automatically-detectable characteristics to subjective human classifications

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Genre Classification

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  1. Genre Classification Andrew HorwitzMUMT621 – 3/12/2014

  2. Genre Classification Grove’s definition: “A class, type or category, sanctioned by convention.” Automatic genre classification is the act of mapping automatically-detectable characteristics to subjective human classifications Non-technical papers in this area also focus on taxonomies and can include manual genre classification

  3. Pachet and Cazaly(2000) • Pachet and Cazaly(2000) analyzed Amazon, AllMusicGuide, and MP3Internet. • AMG: 5 metagenres overlapping 531 sub-genres • Amazon: 18 to 719 • MP3: 16 to 430

  4. Pachet and Cazaly(2000) • Researchers found 70 words similar between all three taxonomies, 198 between two, and 802 that were specific to one taxonomy. • AMG had emphasis on blues (83 sub-genres), MP3 had emphasis on metal • “The analysis shows clearly that there is not much consensus in these classifications, either from the lexical viewpoint (names used), and the structure (depth and structure of the hierarchies). Even large words like ‘Rock’ or ‘Pop’ do not have common definitions!”

  5. Pachet and Cazaly(2000) • Discussion on hierarchy: • Notable taxonomies: • Geography (“International”>“Africa”>“Algeria” in Amazon) • Historical period (“Classical”>”Baroque” or “Classical”>”French Impressionist” in Amazon) • “Aggregation” (“R ’n B/Soul” >“Soul” and “R ’n B/Soul” >“R’nB” in AllMusicGuide) • Design of a new taxonomy: focus on objectivity, independence, and consistency, supporting both similarity and evolution.

  6. Pachet and Cazaly(2000)

  7. Pachet and Cazaly(2000) • Their conclusions: • 78 genres and about 800 similarity relationsto classify 5,000 songs • Taxonomy meant to be seen as a “starting point,” not universal • Some had to be left out; “the decision to reify a term and include it in the taxonomy can only be based on a consensus.” • “When a new genre emerges, it is often, if not always, by the addition of some element to an existing genre.”

  8. Tzanetakis, Essl, and Cook (2001) • Disagreed with Pachet/Cazaly’s hierarchical approach – thought there was enough statistical information to classify genre directly. • One of the first papers on automatic genre classification • Also: Dannenberg, Thom, Watson (1997) explored style

  9. Tzanetakis, Essl, and Cook (2001) • Feature extraction • “Musical surface” features – centroid, rolloff, flux, zero crossing, low energy • Calculated as 20-ms windows averaged over 1s blocks • Rhythmic features – beats are estimated over a ~3-second window, then bpm is plotted on a histogram for the piece. • Histogram is analyzed for specific characteristics (period/amplitude of first peak, ratio of second peak periodicity/relative amplitude of second peak, etc.) • 17 features in total

  10. Tzanetakis, Essl, and Cook (2001)

  11. Aucouturier and Pachet (2003) • Overview of previous attempts, including Pachet’s previous • Acknowledges that objectivity is extremely hard • Taxonomy was also “sensitive to music evolution” • Comments on previously-used techniques • Different genre splits have different classification criteria • Taxonomy objectivity and expandability • Genre classification is usually done by intrinsic/spectral attributes, which is not always how humans do it

  12. Aucouturier and Pachet (2003) • Suggest instead to classify by clustering: find similarities and label them as genres • Intrinsic attributes could be used here, but should be used in the light of “similarity” • Collaborative filtering: using users’ tastes to draw conclusions on where these clusters lie • Co-occurrence: how often songs appear in mix CDs or on radio station programs, etc. • Found that the latter adequately clustered, but the work (from 2001) was not measured for genre.

  13. Burred and Lerch (2003) Automatic hierarchical classification The “curse of dimensionality” – “it is advantageous to reduce the number of features in order to reduce computational costs while keeping similar [or improved] levels of performance.” Machine learns not only how to classify audio, but which classifiers to use for which stages.

  14. Burred and Lerch (2003) Graphic from Burred and Lerch (2003) Separating spoken text from background noise from music, separating music into pop and classical metagenres.

  15. Burred and Lerch (2003) • 90 features total (spectral, rhythmic, timbral) • Reduced to 58 after testing for their robustness to irrelevancies (noise content and signal bandwidth) • Training samples were normalized and mixed with white noise/put through a low-pass filter. • 58 combined into 9 groups (one for each split in the tree), ordered by how accurate the classifications were. • Top 20 features were used for each split.

  16. Contributions of Burred and Lerch (2003) • Found which features were more reliable classifiers for which specific splits • Mel Frequency Cepstral Coefficients, rhythmic regularity and zero crossings in particular • Determined that the hierarchical approach is as effective as the direct approach • More easily expandable (features and genres)

  17. McKay and Fujinaga (2006) • Potential improvements for automatic classification • Using cultural information (or at least more than spectral information) • Multiple genres for or throughout one piece • How do humans classify music? • Seyerlehner, Widmer, and Knees (2011) • Lippens et al. (2004)

  18. References Aucouturier, J., and F. Pachet. 2003. Representing musical genre: a state of the art. Journal of New Music Research 32, no. 1. 83-93. Burred, J., and A. Lerch. 2003. A hierarchical approach to automatic musical genre classification. In Proc. 6th Int. Conf. on Digital Audio Effects’ 03. McKay, C., and I. Fujinaga. 2006. Musical genre classification: Is it worth pursuing and how can it be improved?. In Proceedings of theInternational Symposium on Music Information Retrieval. 101-106. Pachet, F., and D. Cazaly. 2000. A taxonomy of musical genres. Proceedings of theContent-Based Multimedia Information Access Conference (RIAO). Paris. 1238-45. Samson, J. Genre. Grove Music Online. Oxford Music Online. Oxford University Press. Accessed 9 March 9 2014. http://www.oxfordmusiconline.com/subscriber/article/grove/music/40599. Tzanetakis, G., G. Essl, and P. Cook. 2001. Automatic musical genre classification of audio signals.In Proceedings of theInternational Symposium on Music Information Retrieval. Tzanetakis, G., and P. Cook. 2002. Musical genre classification of audio signals. Speech and Audio Processing, IEEE transactions on 10, no. 5. 293-302.

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