This paper presents a technique for Informed Source Separation (ISS) of a single channel mixture, based on the Multiple Input Spectrogram Inversion (MISI) phase estimation method. The reconstruction of the source signals is iterative, alternating between a time-frequency consistency enforcement and a re-mixing constraint. A dual resolution technique is also proposed, for sharper transients reconstruction. The two algorithms are compared to a state-of-the-art Wiener-based ISS technique, on a database of fourteen monophonic mixtures, with standard source separation objective measures. Experimental results show that the proposed algorithms outperform both this reference technique and the oracle Wiener filter by up to 3 dB in distortion, at the cost of a significantly heavier computation.

This paper deals with phase estimation in the framework of underdetermined blind source separation, using an estimated spectrogram of the source and its associated Wiener filter. By thresholding the Wiener mask, two domains are defined on the spectrogram : a confidence domain where the phase is kept as the phase of the mixture, and its complement where the phase is updated with a projection similar to the widely-used Griffin and Lim technique. We show that with this simple technique, the choice of parameters results in a simple trade-off between distortion and interference. Experiments show that this technique brings significant improvements over the classical Wiener filter, while being much faster than other iterative methods.

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The Lines Of Maximum Amplitude (LOMA) of the wavelet transform are used for glottal closure instant detection. Following Kadambe & al. (1992), the wavelet transform modulus maxima can be used for singularity detection. The LOMA method extends this idea. All the lines chaining maxima of a wavelet transform across scales are built. Then a back-tracking procedure allows for selection of the optimal line for each pitch period, the top of which indicates the GCI. The LOMA method is then evaluated by comparing its results to the DYPSA (Naylor & al.) algorithm, with the option of using inverse filtering as preprocessing. The LOMA method compares favorably to DYPSA, particularly on accuracy. One of the advantage of the LOMA method is its ability to deal with variations in the glottal source parameters.

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