This thesis presents a number of modified or novel techniques for the analysis of music audio for the purposes of classifying it according genre or implementing so called `search-by-example’ systems, which recommend music to users and generate playlists and personalised radio stations. Novel procedures for the parameterisation of music audio are introduced, including an audio event-based segmentation of the audio feature streams and methods of encoding rhythmic information in the audio signal. A large number of experiments are performed to estimate the performance of different classification algorithms when applied to the classification of various sets of music audio features. The experiments show differing trends regarding the best performing type of classification procedure to use for different feature sets and segmentations of feature streams.|
A novel machine learning algorithm (MVCART), based on the classic Decision Tree algorithm (CART), is introduced to more effectively deal with multi-variate audio features and the additional challenges introduced by event-based segmentation of audio feature streams. This algorithm achieves the best results on the classification of event-based music audio features and approaches the performance of state-of-the-art techniques based on summaries of the whole audio stream.
Finally, a number of methods of extending music classifiers, including those based on event-based segmentations and the MVCART algorithm, to build music similarity estimation and search procedures are introduced. Conventional methods of audio music search are based solely on music audio profiles, whereas the methods introduced allow audio music search and recommendation indices to utilise cultural information (in the form of music genres) to enhance or scale their recommendations, without requiring this information to be present for every track. These methods are shown to yield very significant reductions in computational complexity over existing techniques (such as those based on the KL-Divergence) whilst providing a comparable or greater level of performance. Not only does the significantly reduced complexity of these techniques allow them to be applied to much larger collections than the KL-Divergence, but they also produce metric similarity spaces, allowing the use of standard techniques for the scaling of metric search spaces.