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|Type:||Artigo de Periódico|
|Title:||Unsupervised Note Activity Detection In Nmf-based Automatic Transcription Of Piano Music|
|Abstract:||Non-negative matrix factorization (NMF) is a traditionalsignal processing technique that allows approximating a measured spectrum as a weighted sum of known spectra. It may be applied to automatic transcription of piano music by assuming that each note is related to a known spectral template. In this model, active notes are related to peaks that can be identified by thresholding. We show that the optimal threshold is highly dependent on the timbre of the particular instrument used to play the analysed audio file. This can cause difficulties to the transcription. We propose an unsupervised technique that allows finding a threshold based on known properties of the audio signal. We show that the proposed technique's performance compares favourably with manual threshold optimization. Moreover, we provide instructions and source codes that allow reproducing all described experiments.|
|Editor:||ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD|
|Citation:||Journal Of New Music Research. ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD, n. 45, n. 2, p. 118 - 123.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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