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Automatic music classification using boosting algorithms and auditory features

With the huge increase in the availability of digital music, it has become more important to automate the task of organizing databases of musical pieces. Currently users have to rely on meta-information such as performer, album, title, as well as more subjective aspects such as genre and mood, to arrange these databases or to create playlists. However, this information must be added manually and it is often inaccurate or incomplete. In this thesis we describe how extract from the audio files features that are meaningful for a classification into categories defined by the user. We show how to extract information using knowledge from signal processing theory, physics of sound, psychoacoustics, human auditory perception, and use it to discriminate high-level characteristics of music, such as music genre. To deal with the very large number of features generated by our approach, we use a multiclass version of the ensemble learner AdaBoost. We describe our algorithm and compare it to other approaches. We present experimental evidence that our method is superior to state-of-the-art algorithms on a hand-labeled database for genre recognition, and describe our performance at the recent MIREX 2005 international contest in music information retrieval, where our algorithm was the best genre classifier among 15 submissions. We also present a novel approach to model the pattern of specific sounds (i.e. speech), using a visual representation of sound. We show how this model can successfully discriminate speech from music at the frame level, using a fraction of the CPU usage of other approaches.