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Comparing Audio Features and Playlist Statistics for Music Classification

Igor Vatolkin 1 Geoffray Bonnin 2 Dietmar Jannach 1
2 KIWI - Knowledge Information and Web Intelligence
LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : In recent years, a number of approaches have been developed for the automatic recognition of music genres, but also more specific categories (styles, moods, personal preferences, etc.). Among the different sources for building classification models, features extracted from the audio signal play an important role in the literature. Although such features can be extracted from any digitized music piece independently of the availability of other information sources, their extraction can require considerable computational costs and the audio alone does not always contain enough information for the identification of the distinctive properties of a musical category. In this work we consider playlists that are created and shared by music listeners as another interesting source for feature extraction and music categorisation. The main idea is that the tracks of a playlist are often from the same artist or belong to the same category, e.g., they have the same genre or style, which allows us to exploit their co-occurrences for classification tasks. In the paper, we evaluate strategies for better genre and style classification based on the analysis of larger collections of user-provided playlists and compare them to a recent classification technique from the literature. Our first results indicate that an already comparably simple playlist-based classifiers can in some cases outperform an advanced audio-based classification technique.
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https://hal.inria.fr/hal-01259247
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Submitted on : Friday, January 22, 2016 - 11:37:25 AM
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Igor Vatolkin, Geoffray Bonnin, Dietmar Jannach. Comparing Audio Features and Playlist Statistics for Music Classification. European Conference on Data Analysis (ECDA), Jul 2014, Bremen, Germany. ⟨hal-01259247⟩

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