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Robust Estimation of Non-Stationary Noise Power Spectrum for Speech Enhancement

van Khanh Mai 1, 2 Dominique Pastor 1, 2 Abdeldjalil Aissa El Bey 3, 2 Raphaël Le Bidan 3, 2
1 Lab-STICC_TB_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
3 Lab-STICC_TB_CACS_COM
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : We propose a novel method for noise power spectrum estimation in speech enhancement. This method called extended-DATE (E-DATE) extends the d-dimensional amplitude trimmed estimator (DATE), originally introduced for additive white gaussian noise power spectrum estimation, to the more challenging scenario of non-stationary noise. The key idea is that, in each frequency bin and within a sufficiently short time period, the noise instantaneous power spectrum can be considered as approximately constant and estimated as the variance of a complex gaussian noise process possibly observed in the presence of the signal of interest. The proposed method relies on the fact that the Short-Time Fourier Transform (STFT) of noisy speech signals is sparse in the sense that transformed speech signals can be represented by a relatively small number of coefficients with large amplitudes in the time-frequency domain. The E-DATE estimator is robust in that it does not require prior information about the signal probability distribution except for the weak-sparseness property. In comparison to other state-of-the-art methods, the E-DATE is found to require the smallest number of parameters (only two). The performance of the proposed estimator has been evaluated in combination with noise reduction and compared to alternative methods. This evaluation involves objective as well as pseudo-subjective criteria.
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van Khanh Mai, Dominique Pastor, Abdeldjalil Aissa El Bey, Raphaël Le Bidan. Robust Estimation of Non-Stationary Noise Power Spectrum for Speech Enhancement. IEEE Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2015, 23 (4), pp.670 - 682. ⟨10.1109/TASLP.2015.2401426⟩. ⟨hal-01216071⟩

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