de Leon, Franz

Martinez, Kirk

English

Southampton (e-Prints Soton)

Sound Similarity: Adding a New Dimension to Music Discovery Franz de Leon, Kirk Martinez, Robert Damper  Electronics and Computer Science  How do I search for music? Requirements • Search database for similar tracks to a query song • Algorithm should be fast and scalable  • System can handle wide variety of music genres  Background   “Digital downloads driving recorded music industry towards recovery” [1]         • Global recorded music revenues up 0.3%, boosted by downloads, subscription and other channels • Digital revenues up 9%, with major music services now open in more than 100 markets • Music is helping fuel the digital economy, but barriers to growth need to be addressed  Searching similar music  • Collaborative filtering  • Social tag data  • Expert metadata  • Direct analysis of audio content  Research objectives   The research aims to investigate how content-based methods can be used to perform music similarity estimation. This work hopes to complement music search engines to provide an exciting journey of music discovery.  Key issues  •What features are essential? •Why are these features essential? Music retrieval flowchart  Feature  extraction  Nearest tracks Proposed system  Initial results  Acknowledgements Franz de Leon is supported by the Engineering Research and Development for Technology Faculty Development Program of the University of the Philippines, and DOST  References [1] Digital Music Report 2013,  Feb 26, 2013 http://www.ifpi.org/content/section_resources/dmr2013.html [2] American Standards Association 1960, 45 [3] W. Penny, “Kullback-Liebler Divergences of Normal, Gamma,  Dirichlet and Wishart Densities,” 2001. [4] D. Schnitzer, A. Flexer, and G. Widmer, “A Filter-and-Refine Method for Fast Similarity Search in Millions of Tracks,” in ISMIR 2009, 2009, no. April, pp. 537-542. [5] G. Tzanetakis and P. Cook, “Musical genre classification of audio signals,” IEEE Transactions on Speech and Audio Processing, vol. 10, no. 5, pp. 293-302, Jul. 2002.  ? 1990  Present production distribution consumption Digital Music Revolution Query song Mapped Timbre Models Timbre Models Training set Mapped Timbre Models Euclidean distance SKL Divergence [6] Candidate songs Timbre Models BROAD SEARCH FINE SEARCH •How are these features summarized? •How are the distances between features computed? •What statistics are used to evaluate the algorithm? query song extract features compute features similarity retrieve nearest songs  music features database song search results features schema song database •How are these features extracted? With the growing appetite for digital music, there is a need for new applications for browsing, organising, discovering  and generating playlists. Timbre is “that attribute of auditory sensation in terms of which a listener can judge that two sounds, similarly presented and having the same loudness and pitch, are dissimilar.” [2] Hence, it is to develop a computational model that         the salient features of timbre.   crucial captures Extracted Features Per Segment Description Mel-frequency cepstral coefficients spectral shape Spectral contrast  difference between spectral peak and valley Spectral flux  change in energy between successive frame others  derivative features from the magnitude spectrum 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-1-0.500.51Audio waveformtime (sec)amplitude0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-1-0.500.51Segmented audio waveformtime (sec)amplitude0 2000 4000 6000 8000 10000 120000100200300400500600700800frequency (Hertz)magnitudeSegmented audio magnitude spectrum0 0.5 1 1.5 2 2.5 3 3.5 4 4.50200040006000800010000Spectrogramtime(sec)frequency (Hz)Timbre Models The extracted feature vectors per song are summarized by computing their mean and covariance matrix .  Mapped Timbre Models The timbre features are mapped to Euclidean space using a modified Fastmap algorithm [4].  Retrieval  Music retrieval is done in two stages: broad search and fine search. Broad search quickly generates a list of candidate similar songs to a query song using a simple Euclidean distance between features. Fine search is performed on the candidate songs using  SKL divergence [3]. Precision is the ratio of the songs retrieved that are similar to the query song. The plot below shows the results for a database [5] with 1000 songs from 10 genres, 100 songs per genre. The system works best on classical, pop and metal. Moreover, the proposed  system takes only 35 msec. to retrieve  tracks. 4.3 4.7  4.8 5.1 5.6 0 1 2 3 4 5 6 2008  2009  2010  2011  2012 US$ billions Global digital music revenues 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Precision without Fastmap with Fastmap satisfactory precision level 

Sound similarity: adding a new dimension to music discovery

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