Perceptually-constrained generalized singular value decomposition-based approach for enhancing speech corrupted by colored noise

Abstract

In a previous work, we have successfully integrated the transformation-based signal subspace technique with the generalized singular value decomposition (GSVD) algorithm to develop an improved speech enhancement framework [1]. In this paper, we further incorporate the perceptual masking effect of the psychoacoustics model as extra constraints of the previously proposed GSVD-based algorithm to obtain improved sound feature, and furthermore make sure the undesired residual noise to be nearly unperceivable. Both subjective listening tests and spectrogram-plot comparison showed that the closed-form solution developed here can offer significantly better speech quality than either the conventional spectral subtraction algorithm or the previously proposed GSVD-based technique, regardless of whether the additive noise is white or not.

A Perceptually Constrained GSVD-Based Approach for Enhancing Speech Corrupted by Colored Noise

“…Though the GSVD-based approach mentioned previously has been shown to provide better performance than the other popular enhancement approaches [13], some musical noise is still perceivable in the enhanced speech under lower SNR conditions. To obtain better sounding conditions, we further propose to integrate the masking properties of human auditory system into the GSVD-based approach to establish an improved framework for speech enhancement in this paper [18], referred to as the perceptually constrained GSVD (PCGSVD)-based approach here.…”

“…In this section, first we briefly summarize the procedure for evaluating the human auditory masking thresholds (AMTs) in frequency domain, following that we offer two series of PCGSVD-based approach (PCGSVD-MVE and PCGSVD-LSE). Furthermore, because the PCGSVD-based approach operates in the generalized singular domain, a transformation of AMTs between the frequency domain and the generalized singular domain is proposed [18], with which closed-form solutions for the PCGSVD-MVE-and PCGSVD-LSE-based subspace approaches can be obtained. Finally, in Section III-F, we discuss the influence of the scaling factor , as defined in Section II-B for obtaining the matrix , on the performance evaluation of the proposed enhancement approaches.…”

“…By incorporating the auditory masking effect into the GSVD-MVE-based subspace approach to further suppress the perceivable residual noise, the goal here is to find an optimal transformation matrix for which not only the Frobenius distance between the two matrices and is minimum, but under the constraints that the normalized energies are not greater than the transformed AMTs for the first projections ( is the dimensionality of the signal subspace of the matrices and here) of the residual noise signal (i.e., ), and zero for the rest projections. In other words, the residual noise components cannot be perceived by the human ear subject to (10) where the vectors and are, respectively, the th column and row, , of the matrices and obtained in ( 2) and (3), and is the AMTs but transformed to the generalized singular domain, which can be evaluated by the procedures present below [18]. Everything else is the same as the procedures summarized in Section II.…”

“…Although this GSVD-based speech enhancement approach has been shown to provide better performance than the previous SVD-based approach, some musical noise is still perceivable in the enhanced speech under lower SNR conditions [12], [13]. This is why the auditory masking thresholds (AMTs) in human auditory functions were further integrated with the above GSVD-based algorithm to establish an improved framework for speech enhancement in this paper [18], referred to as the perceptually constrained GSVD (PCGSVD)-based approach here. Because this PCGSVD-based approach operates in the generalized singular domain, whereas the conventional auditory masking thresholds (AMTs) are well defined in frequency domain, the previously proposed transformation between the frequency domain and the eigen domain [19] is extended to be performed between the frequency domain and the generalized singular domain [18], with which a closed-form solution for the PCGSVD-based speech enhancement approach is obtained.…”

“…This is why the auditory masking thresholds (AMTs) in human auditory functions were further integrated with the above GSVD-based algorithm to establish an improved framework for speech enhancement in this paper [18], referred to as the perceptually constrained GSVD (PCGSVD)-based approach here. Because this PCGSVD-based approach operates in the generalized singular domain, whereas the conventional auditory masking thresholds (AMTs) are well defined in frequency domain, the previously proposed transformation between the frequency domain and the eigen domain [19] is extended to be performed between the frequency domain and the generalized singular domain [18], with which a closed-form solution for the PCGSVD-based speech enhancement approach is obtained. Experimental results based on various objective and subjective tests (e.g., time/frequency domain evaluations, speech recognition accuracies, paired-utterance listening comparison, mean opinion score (MOS) rating, etc.)…”