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Automatic Chord Recognition Based on Bass Estimation and Pitch Classes Classification by Volume

Automatic chord recognition in audio is difficult. Substantial research have been carried out to achieve it, most recently using deep neural networks. In contrast, in this paper we propose an algorithm based on human musical knowledge and expertise. The algorithm takes as input the instantaneous volume of the notes and the chord start and end times. For each chord, the algorithm ranks the pitch classes according to their total volume. The ‘word’ formed by this ranking is searched in a manually created dictionary and the appropriate ‘meaning’ is chosen based on a robust estimation of the bass. The algorithm was tested using volume estimates produced by a external program and manually defined chord boundaries. The results show recognition rates of 93% for the root, 90% for major and minor triads, 90% with inversions, 82% with sevenths, and 79% with both.