Unsupervised Music Genre Classification with a Model-Based Approach

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


New music genres emerge constantly resulting from the influence of existing genres and other factors. In this paper we propose a data-driven approach which is able to cluster and classify music samples according to their type/category. The clustering method uses no previous knowledge on the genre of the individual samples or on the number of genres present in the dataset. This way, musictaggingis not imposed by the users’ subjective knowledge about music genres, which may also be outdated. This method follows a model-based approach to group music samples into different clusters only based on their audio features, achieving a perfect clustering accuracy (100%) when tested with 4 music genres. Once the clusters are learned, the classification method can categorize new music samples according to the previously learned created groups. By using Mahalanobis distance, this method is not restricted to spherical clusters, achieving promising classification rates: 82%.
Original languageUnknown
Title of host publicationLecture Notes in Computer Science
Publication statusPublished - 1 Jan 2011
Event15th Portuguese Conference on Artificial Intelligence (EPIA 2011) -
Duration: 1 Jan 2011 → …


Conference15th Portuguese Conference on Artificial Intelligence (EPIA 2011)
Period1/01/11 → …

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