Abstract
The availability of large music repositories poses challenging research problems, which are also related to the identification of different performances of music scores. This paper presents a methodology for music identification based on hidden Markov models. In particular, a statistical model of the possible performances of a given score is built from the recording of a single performance. To this end, the audio recording undergoes a segmentation process, followed by the extraction of the most relevant features of each segment. The model is built associating a state for each segment and by modeling its emissions according to the computed features. The approach has been tested with a collection of orchestral music, showing good results in the identification and tagging of acoustic performances.
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Miotto, R., Orio, N. (2007). Automatic Identification of Music Works Through Audio Matching. In: Kovács, L., Fuhr, N., Meghini, C. (eds) Research and Advanced Technology for Digital Libraries. ECDL 2007. Lecture Notes in Computer Science, vol 4675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74851-9_11
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DOI: https://doi.org/10.1007/978-3-540-74851-9_11
Publisher Name: Springer, Berlin, Heidelberg
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