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Artist Identification Based on Song Analysis

Artist Identification Based on Song Analysis. Motivation. People have a large collection of digital music Although metadata about songs are available from other sources it would be nice to be able to recognize an artist from the song itself. Previous Work.

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Artist Identification Based on Song Analysis

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  1. Artist Identification Based on Song Analysis

  2. Motivation • People have a large collection of digital music • Although metadata about songs are available from other sources it would be nice to be able to recognize an artist from the song itself.

  3. Previous Work • Michael Mandel and Dan Ellis have come up with a scheme to create Gaussian Model using the mean and variances of 20 features of MFCC vectors calculated for all the frames of a song . • Once this Gaussian model is formed they compare the similar Gaussian Model of the test song using a SVM classifier.

  4. Previous Work… • With this method they achieved 69% to 84% accuracy in detecting artists • http://www.ee.columbia.edu/~dpwe/pubs/ismir05-svm.pdf

  5. Proposed Extension • The MFCC frames of a song that were chosen to form a Gaussian model were random. I would like to use the Music similarity measures to select the MFCC frames of a song. • The paper by Matthew Cooper and Jonathan Foote on Automatic Music Summarization by Similarity analysis provides a way for getting an audio thumbnail for a song • http://www.fxpal.com/publications/FXPAL-PR-02-171.pdf

  6. Proposed Extension… • For artist identification purpose the frames which has the artists voice has the most information • MFCC features are good at identifying the spectral characteristics of speech • The assumption is that the frames of songs having high similarity scores will probably have artists voice in it.

  7. Proposed Extension… • I plan to build a similarity matrix comparing the MFCC vectors of each frame of the song with every other frame • The distance between the frames will be calculated based on the dot product of the feature vectors • The Frames having the highest similarity scores across frames of a song will be chosen to build a Gaussian model

  8. Proposed Extension… • I plan to experiment with how many frames I select using this similarity metric for song level features and then using a SVM classifier . • Mandel & Ellis in their paper had tried to build an artist model based on all the songs from a training set and did not get good results

  9. Proposed Extension… • I believe that a successful artist model can be built if we select the right frames from the songs in the training set. • I plan to build a Gaussian model for an artist by selecting the frames from the song set with highest similarity scores.

  10. Evaluation • I plan to evaluate my techniques on USPOP2002 dataset • http://www.ee.columbia.edu/~dpwe/research/musicsim/uspop2002.html • Evaluation criteria based on training times and the success of identifying artists.

  11. Challenges • Computing similarity matrix may be computationally inefficient

  12. References • http://www.geocities.com/sivaram@snet.net

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