Segmental Mean Square Error Research Articles

A new speech enhancement adaptive algorithm based on fullband–subband MSE switching

This paper presents a new fullband–subband switching adaptive speech enhancement algorithm, based on mean square error estimation. The proposed algorithm is able to automatically switch between two adaptive filtering algorithms, i.e. the two-channel fullband normalized least mean square (TC-FNLMS) algorithm and the two-channel subband normalized least mean square (TC-SNLMS), where, the proposed switching mechanism leads to a significant improvement in the convergence speed performance of the proposed algorithm. To confirm the efficiency and the good performances of the proposed algorithm in comparison with the fullband and subband versions of the two channel NLMS algorithm, several experiments were carried out in terms of the segmental signal-to-noise-ratio (SegSNR), segmental mean square error (SegMSE), system mismatch (SM) and cepstral distance (CD).

A new efficient two-channel fast transversal adaptive filtering algorithm for blind speech enhancement and acoustic noise reduction

This paper addresses the problem of speech enhancement and acoustic noise reduction by blind structures. Recently, the backward blind source separation (BBSS) structure has shown efficiency in cancelling the acoustic noise and improving corrupted speech signals form very noisy observations without any a priori information of source signals. In this paper, we propose a new algorithm based on the combination between the BBSS structure and the simplified fast transversal filter (SFTF) algorithm. The proposed two-channel simplified fast transversal filter (TCSFTF) algorithm succeeded an important blind improvement of steady state and convergence speed performances in diverse noisy situations when only the noisy signals are known. The performances of the new TCSFTF algorithm are compared with four state-of-the-art algorithms in different noisy conditions. This comparison is evaluated in terms of cepstral distance (CD), system mismatch (SM), segmental signal to noise ratio (SegSNR), and segmental mean square error (SegMSE) criteria.

New efficient two channel forward set-membership partial-update NLMS algorithms for blind speech enhancement and acoustic noise reduction

This paper addresses the problem of speech enhancement by partial adaptive algorithms combined with the forward blind source separation (FBSS) structure. We propose three new two-channel forward partial update algorithms: (i) The first algorithm is based on the use of the correlation properties to adapt and control the variable step-size parameter of adaptive cross-filters of the FBSS structure, this algorithm is called the two channel correlated set membership partial update NLMS (TC-SM-PU-NLMScor). (ii) The second algorithm is based on the use of a new coefficients update relations to control the normalization energy of the adaptive NLMS cross filters of the FBSS structure. This algorithm is called the two channel set member ship partial update regularized NLMS (TC-SM-PU-RNLMS). (iii) The third algorithm is a combination of proposed 1 (TC-SM-PU-NLMScor) and proposed 2 (TC-SM-PU-RNLMS), this algorithm is called the two channel correlated set membership partial update regularized NLMS (TC-SM-PU-RNLMScor). In this paper, we show the obtained results of the comparison between the three proposed algorithms and the original two channel correlated set membership partial NLMS (TC-SM-PU-NLMS) algorithm. This comparison is expressed in terms of objective criteria as the System Mismatch (SM), the Segmental Mean Square Error (SegMSE) and the Segmental SNR (SegSNR).

An efficient wavelet-based adaptive filtering algorithm for automatic blind speech enhancement

In this paper, we address the problem of speech enhancement by adaptive filtering algorithms. A particular attention has been paid to the backward blind source separation (BBSS) algorithm and its use in crosstalk resistant speech enhancement applications. In this paper, we propose to implement the BBSS algorithm in the wavelet-domain. The proposed backward wavelet BBSS (WBBSS) algorithm is then used in speech enhancement application when important crosstalk interferences are presents. The new WBBSS algorithm shows better performances in terms of convergence speed and steady state in comparison with the classical BBSS one. The performances properties of the proposed algorithm are evaluated in term of segmental SNR (SegSNR), segmental mean square error (SegMSE), and cepstral distance (CD) criteria. The obtained results have confirmed the best performance of the proposed WBBSS algorithm in a lot of situations when blind noisy observations are available.

A dual fast NLMS adaptive filtering algorithm for blind speech quality enhancement

This paper addresses the problem of acoustic noise reduction and speech enhancement in new telecommunications systems by adaptive filtering algorithms. Recently, a particular attention has been made to the blind source separation (BSS) approach applied for the separation of speech and noise components. The BSS application has inherits the good properties of the adaptive filtering algorithm to give more intelligible enhanced speech signal in term of quality. In this paper, we propose a new dual forward BSS algorithm that is based on signal prediction to give an automatic algorithm with a very fine behavior at the output. This algorithm is called the dual fast normalized least mean square (DFNLMS) algorithm. This algorithm has been tested in various noisy conditions and has shown its superiority in terms of the following objective criteria: cepstral distance (CD), segmental signal-to-noise-ratio (SegSNR), segmental mean square error (SegMSE), and system mismatch (SM). A comparison with other competitive and state-of-the-art algorithms is also presented in this paper.

An efficient frequency-domain adaptive forward BSS algorithm for acoustic noise reduction and speech quality enhancement

In this paper, we consider the speech enhancement and acoustic noise reduction problem in a moving car through a blind source separation scheme employing two loosely spaced microphones. We propose a new efficient frequency domain-symmetric adaptive decorrelation (FD-SAD) algorithm that removes punctual noise components from noisy speech signals. The FD-SAD algorithm is combined with the forward blind source separation FBSS structure to enhance the performances of its time-domain symmetric adaptive decorelating (TD-SAD) version. The proposed algorithm has a good tracking behaviour and fast convergence speed even in very noisy conditions with loosely spaced microphones. Intensive experiments have been done on the newly proposed algorithm in terms of the Segmental Signal-to-Noise-Ratio (SegSNR), the System Mismatch (SM), the Segmental Mean Square Error (SegMSE), and the Cepstral Distance (CD) criteria. The comparison results with the state-of-the-art algorithms have highlighted the excellent performance of the proposed algorithm, and have shown its ability to completely remove the correlated noise components from speech signal even in very noisy conditions when controlled by a voice activity detector.