First stereo audio source separation evaluation campaign: data, algorithms and results
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
The 2010 signal separation evaluation campaign (SiSEC2010): audio source separation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Audio Denoising by Time-Frequency Block Thresholding
IEEE Transactions on Signal Processing
Informed source separation through spectrogram coding and data embedding
Signal Processing
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Wiener filtering is one of the most widely used methods in audio source separation. It is often applied on time-frequency representations of signals, such as the short-time Fourier transform (STFT), to exploit their short-term stationarity, but so far the design of the Wiener time-frequency mask did not take into account the necessity for the output spectrograms to be consistent, i.e., to correspond to the STFT of a time-domain signal. In this paper, we generalize the concept of Wiener filtering to time-frequency masks which can involve manipulation of the phase as well by formulating the problem as a consistency-constrained Maximum-Likelihood one. We present two methods to solve the problem, one looking for the optimal time-domain signal, the other promoting consistency through a penalty function directly in the time-frequency domain. We show through experimental evaluation that, both in oracle conditions and combined with spectral subtraction, our method outperforms classical Wiener filtering.