Computer analysis of similarities between albums in popular music

• Unsupervised analysis of albums in popular music is presented.
• The analysis is done by a first step of transforming the audio files of the songs to 2D spectrograms.
• The method was able to sort the albums of bands in an order that is very close to their chronological order.
• The spectrogram analysis provided more informative analysis than audio features extracted directly from the audio files.

Analysis of musical styles is a complex cognitive task normally performed by music fans and critics, and due to the multi-dimensional nature of music data can be considered a challenging task for computing machines. Here we propose an automatic quantitative method that can analyze similarities between the sound of popular music albums in an unsupervised fashion. The method works by first converting the music samples into two-dimensional spectrograms, and then extracting a large set of 2883 2D numerical content descriptors from the raw spectrograms as well as 2D transforms and compound transforms of the spectrograms. The similarity between each pair of samples is computed using a variation of the Weighted K-Nearest Neighbor scheme, and a phylogeny is then used to visualize the differences between the albums. Experimental results show that the method was able to automatically organize the albums of The Beatles by their chronological order, and also unsupervisely arranged albums of musicians such as U2, Queen, ABBA, and Tears for Fears in a fashion that is largely in agreement with their chronological order and musical styles.

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