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- W189345193 abstract "We present a system for adaptive spectral basis decomposition that learns to identify independent spectral features given a sequence of short-term Fourier spectra. When applied to recordings of polyphonic piano music, the individual notes are identified as salient features, and hence each short-term spectrum is decomposed into a sum of note spectra; the resulting encoding can be used as a basis for polyphonic transcription. The system is based on a probabilistic model equivalent to a form of noisy independent component analysis (ICA) or sparse coding with non-negativity constraints. We introduce a novel modification to this model that recognises that a short-term Fourier spectrum can be thought of as a noisy realisation of the power spectral density of an underlying Gaussian process, where the noise is essentially multiplicative and non-Gaussian. Results are presented for an analysis of a live recording of polyphonic piano music." @default.
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- W189345193 date "2004-01-01" @default.
- W189345193 modified "2023-10-03" @default.
- W189345193 title "Polyphonic music transcription by non-negative sparse coding of power spectra" @default.
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