A New Method for Calculating Music Similarity
نویسندگان
چکیده
We introduce a new technique for calculating the perceived similarity of two songs based on their spectral content. Our method uses a set of hidden Markov Models to model the temporal evolution of a song. We then compute a dissimilarity distance measure based on finding log likelihood probabilities using Monte Carlo sampling. This method is compared to a previously established technique that performs frame clustering using Gaussian mixture models. Each method’s performance is analyzed on a music catalog of 105 songs, and performance is subjectively evaluated.
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