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- W4287669064 abstract "We revisit the source image estimation problem from blind source separation (BSS). We generalize the traditional minimum distortion principle to maximum likelihood estimation with a model for the residual spectrograms. Because residual spectrograms typically contain other sources, we propose to use a mixed-norm model that lets us finely tune sparsity in time and frequency. We propose to carry out the minimization of the mixed-norm via majorization-minimization optimization, leading to an iteratively reweighted least-squares algorithm. The algorithm balances well efficiency and ease of implementation. We assess the performance of the proposed method as applied to two well-known determined BSS and one joint BSS-dereverberation algorithms. We find out that it is possible to tune the parameters to improve separation by up to 2 dB, with no increase in distortion, and at little computational cost. The method thus provides a cheap and easy way to boost the performance of blind source separation." @default.
- W4287669064 created "2022-07-25" @default.
- W4287669064 creator A5020401831 @default.
- W4287669064 date "2020-09-11" @default.
- W4287669064 modified "2023-09-24" @default.
- W4287669064 title "Generalized Minimal Distortion Principle for Blind Source Separation" @default.
- W4287669064 doi "https://doi.org/10.48550/arxiv.2009.05288" @default.
- W4287669064 hasPublicationYear "2020" @default.
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