This paper describes a novelmusic similarity calculation method that is based on the instrumentation of music pieces. The approach taken here is based on the idea that sparse representations of musical audio signals are a rich source of information regarding the elements that constitute the observed spectra. We propose a method to extract feature vectors based on sparse representations and use these to calculate a similarity measure between songs. To train a dictionary for sparse representations from a large amount of training data, a novel dictionary-initialization method based on agglomerative clustering is proposed. An objective evaluation shows that the new features improve the performance of similarity calculation compared to the standard mel-frequency cepstral coefficients features.
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