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Automatic Music Transcription Using Low Rank Non-Negative Matrix Decomposition

O'Brien, Cian and Plumbley, Mark (2017) Automatic Music Transcription Using Low Rank Non-Negative Matrix Decomposition In: 25th European Signal Processing Conference EUSIPCO2017, 28th August - 2nd September 2017, Kos island, Greece.

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Automatic Music Transcription (AMT) is concerned with the problem of producing the pitch content of a piece of music given a recorded signal. Many methods rely on sparse or low rank models, where the observed magnitude spectra are represented as a linear combination of dictionary atoms corresponding to individual pitches. Some of the most successful approaches use Non-negative Matrix Decomposition (NMD) or Factorization (NMF), which can be used to learn a dictionary and pitch activation matrix from a given signal. Here we introduce a further refinement of NMD in which we assume the transcription itself is approximately low rank. The intuition behind this approach is that the total number of distinct activation patterns should be relatively small since the pitch content between adjacent frames should be similar. A rank penalty is introduced into the NMD objective function and solved using an iterative algorithm based on Singular Value thresholding. We find that the low rank assumption leads to a significant increase in performance compared to NMD using �- divergence on a standard AMT dataset.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Date : 2017
Copyright Disclaimer : Copyright EUSIPCO 2017
Depositing User : Jane Hindle
Date Deposited : 17 Aug 2017 09:48
Last Modified : 03 Sep 2017 02:08

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