- Research Article
- Open Access
A Model-Based Approach to Constructing Music Similarity Functions
EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 024602 (2006)
Several authors have presented systems that estimate the audio similarity of two pieces of music through the calculation of a distance metric, such as the Euclidean distance, between spectral features calculated from the audio, related to the timbre or pitch of the signal. These features can be augmented with other, temporally or rhythmically based features such as zero-crossing rates, beat histograms, or fluctuation patterns to form a more well-rounded music similarity function. It is our contention that perceptual or cultural labels, such as the genre, style, or emotion of the music, are also very important features in the perception of music. These labels help to define complex regions of similarity within the available feature spaces. We demonstrate a machine-learning-based approach to the construction of a similarity metric, which uses this contextual information to project the calculated features into an intermediate space where a music similarity function that incorporates some of the cultural information may be calculated.
Whitman B, Lawrence S: Inferring descriptions and similarity for music from community metadata. Proceedings of the International Computer Music Conference (ICMC '02), September 2002, Göteborg, Sweden 591–598.
Hu X, Downie JS, West K, Ehmann AF: Mining music reviews: promising preliminary results. Proceedings of 6th International Conference on Music Information Retrieval (ISMIR '05), September 2005, London, UK 536–539.
Gracenote Gracenote Playlist. 2005. https://doi.org/www.gracenote.com/gn_products/
Gracenote Gracenote MusicID. 2005. https://doi.org/www.gracenote.com/gn_products/
Wang A: Shazam Entertainment. ISMIR 2003 - Presentation. https://doi.org/ismir2003.ismir.net/
Logan B, Salomon A: A music similarity function based on signal analysis. Proceedings of IEEE International Conference on Multimedia and Expo (ICME '01), August 2001, Tokyo, Japan 745–748.
Pampalk E, Flexer A, Widmer G: Improvements of audio-based music similarity and genre classificaton. Proceedings of 6th International Conference on Music Information Retrieval (ISMIR '05), September 2005, London, UK 628–633.
Pampalk E, Pohle T, Widmer G: Dynamic playlist generation based on skipping behavior. Proceedings of 6th International Conference on Music Information Retrieval (ISMIR '05), September 2005, London, UK 634–637.
Aucouturier J-J, Pachet F: Music similarity measures: what's the use? Proceedings of the 3rd International Conference on Music Information Retrieval (ISMIR '02), October 2002, Paris, France
Ragno R, Burges CJC, Herley C: Inferring similarity between music objects with application to playlist generation. Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, November 2005, Singapore, Republic of Singapore
Downie JS, West K, Ehmann AF, Vincent E: The 2005 music information retrieval evaluation exchange (MIREX 2005): preliminary overview. Proceedings of 6th International Conference on Music Information Retrieval (ISMIR '05), September 2005, London, UK 320–323.
Downie JS: MIREX 2005 Contest Results. https://doi.org/www.music-ir.org/evaluation/mirex-results/
Kuncheva L: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, New York, NY, USA; 2004.
Jiang D-N, Lu L, Zhang H-J, Tao J-H, Cai L-H: Music type classification by spectral contrast feature. Proceedings of IEEE International Conference on Multimedia and Expo (ICME '02), August 2002, Lausanne, Switzerland 1: 113–116.
West K, Cox S: Finding an optimal segmentation for audio genre classification. Proceedings of 6th International Conference on Music Information Retrieval (ISMIR '05), September 2005, London, UK 680–685.
Tzanetakis G: Marsyas: a software framework for computer audition. October 2003.https://doi.org/marsyas.sourceforge.net/
Tzanetakis G, Cook P: Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing 2002,10(5):293-302. 10.1109/TSA.2002.800560
West K: MIREX Audio Genre Classification. 2005.https://doi.org/www.music-ir.org/evaluation/mirex-results/articles/audio_genre/
Lidstone GJ: Note on the general case of the bayeslaplace formula for inductive or a posteriori probabilities. Transactions of the Faculty of Actuaries 1920, 8: 182–192.
Chalmers M: A linear iteration time layout algorithm for visualising high-dimensional data. Proceedings of the 7th IEEE Conference on Visualization, October 1996, San Francisco, Calif, USA
Kruskal JB: Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 1964,29(1):1-27. 10.1007/BF02289565
Magnatune : Magnatune: MP3 music and music licensing (royalty free music and license music). 2005.https://doi.org/magnatune.com/
Downie JS: M2K (Music-to-Knowledge): a tool set for MIR/MDL development and evaluation. 2005.https://doi.org/www.music-ir.org/evaluation/m2k/index.html
National Center for Supercomputing Applications ALG: D2K Overview. 2004. https://doi.org/alg.ncsa.uiuc.edu/do/tools/d2k
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West, K., Lamere, P. A Model-Based Approach to Constructing Music Similarity Functions. EURASIP J. Adv. Signal Process. 2007, 024602 (2006). https://doi.org/10.1155/2007/24602
- Information Technology
- Euclidean Distance
- Spectral Feature
- Feature Space
- Quantum Information