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Automatic music classification aims at grouping unknown songs in predefined categories such as music genre or induced emotion. To obtain perceptually relevant results, it is needed to design appropriate features that carry important information for semantic inference. In this paper, we explore novel features and evaluate them in a task of music automatic tagging. The proposed features span various...
In this paper, we introduce the use of conditional random fields (CRFs) for the audio-to-score alignment task. This framework encompasses the statistical models which are used in the literature and allows for more flexible dependency structures. In particular, it allows observation functions to be computed from several analysis frames. Three different CRF models are proposed for our task, for different...
In this paper we review the acoustic features used for music-to-score alignment and study their influence on the performance in a challenging alignment task, where the audio data is polyphonic and may contain percussion. Furthermore, as we aim at using “real world” scores, we follow an approach which does exploit the rhythm information (considered unreliable) and test its robustness to score errors...
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