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In this paper, various regularizations on the room impulse response
(RIR) are proposed to obtain better single-channel speech derever-
beration in the non-negative matrix factorization (NMF) framework.
The regularizations on the RIR are motivated by the spectral domain
representation of the RIR. To obtain better estimates of the RIR and
clean speech, we propose three modifications (i) to obtain a sparse
RIR (ii) a frequency envelop constrained RIR and (iii) to include the
early part of the RIR. The performance of the proposed regularizers

This paper provides a detailed comparison study between three different vehicles’ Bluetooth built-in noise cancellation filter with two widely used techniques in speech enhancement, Spectral Subtraction (SS) and Wiener filtering (WF). The main purpose is to determine if any of these two filters provide superior audio quality over the built-in filter.

This paper provides a detailed comparison study between three different vehicles’ Bluetooth built-in noise cancellation filter with two widely used techniques in speech enhancement, Spectral Subtraction (SS) and Wiener filtering (WF). The main purpose is to determine if any of these two filters provide superior audio quality over the built-in filter.

The ITU-T Recommendation G.722 about subband adaptive differential pulse code modulation (SB-ADPCM) is the mandatory wideband speech codec in the new generation digital enhanced cordless telephony (NG-DECT). Although in ADPCM the difference signal instead of the original signal is quantized and adaptive prediction is employed, redundancy is yet observed within the quantized samples. In this paper we apply a soft-decision speech decoding technique which exploits this redundancy in terms of a priori knowledge and the channel reliability information to NG-DECT.

We propose sparse reconstruction techniques to improve the quality and / or reduce the bit-rate of standard speech coders. To that end, we assume signal sparsity in some transform domain and formulate the problem of reconstructing the original signal in terms of constrained l1-norm minimization. We use modern primal-dual methods in order to solve the resulting non-smooth convex optimization problem. Experiments show that with the proposed sparse reconstruction method the instrumentally predicted speech quality can be largely improved.