In this paper, we investigate the derivation of musical structures directly from signal analysis with the aim of generating visual and audio summaries. From the audio signal, we first derive features - static features (MFCC, chromagram) or proposed dynamic features. Two approaches are then studied in order to derive automatically the structure of a piece of music. The sequence approach considers the audio signal as a repetition of sequences of events. Sequences are derived from the similarity matrix of the features by a proposed algorithm based on a 2D structuring filter and pattern matching. The state approach considers the audio signal as a succession of states. Since human segmentation and grouping performs better upon subsequent hearings, this natural approach is followed here using a proposed multi-pass approach combining time segmentation and unsupervised learning methods. Both sequence and state representations are used for the creation of an audio summary using various techniques. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Peeters, G. (2004). Deriving musical structures from signal analysis for music audio summary generation: “Sequence” and “state” approach. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2771, 143–166. https://doi.org/10.1007/978-3-540-39900-1_14
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