Signal Processing Methods for Audio Classification and Music Content Analysis

Signal processing methods for audio classification and music content analysis are developed in this thesis. Audio classification is here understood as the process of assigning a discrete category label to an unknown recording. Two specific problems of audio classification are considered: musical instrument recognition and context recognition. In the former, the system classifies an audio recording according to the instrument, e.g. violin, flute, piano, that produced the sound. The latter task is about classifying an environment, such a car, restaurant, or library, based on its ambient audio background. In the field of music content analysis, methods are presented for music meter analysis and chorus detection. Meter analysis methods consider the estimation of the regular pattern of strong and weak beats in a piece of music. The goal of chorus detection is to locate the chorus segment in music which is often the catchiest and most memorable part of a song. These are among the most important and readily commercially applicable content attributes that can be automatically analyzed from music signals. For audio classification, several features and classification methods are proposed and evaluated. In musical instrument recognition, we consider methods to improve the performance of a baseline audio classification system that uses mel-frequency cepstral coefficients and their first derivatives as features, and continuous-density hidden Markov models (HMMs) for modeling the feature distributions. Two improvements are proposed to increase the performance of this baseline system. First, transforming the features to a base with maximal statistical independence using independent component analysis. Secondly, discriminative training is shown to further improve the recognition accuracy of the system. For musical meter analysis, three methods are proposed. The first performs meter analysis jointly at three different time scales: at the temporally atomic tatum pulse level, at the tactus pulse level, which corresponds to the tempo of a piece, and at the musical measure level. The features obtained from an accent feature analyzer and a bank of combfilter resonators are processed by a novel probabilistic model which rep-

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