Chengshi Zheng

Two-channel post-filtering based on adaptive smoothing and noise properties

This paper studies the statistical properties of the gain functions, which are often used for two-channel post-filtering (TC-PF) algorithms. We reveal that the smoothing factor has a significant impact on both noise reduction and musical noise. When the smoothing factor increases, noise reduction can be improved and musical noise can be reduced simultaneously. However, the smoothing factor could not be too close to one because the system can only be assumed to be time-invariant for short durations. To solve this problem, this paper proposes an adaptive smoothing scheme by detecting the sudden change of the system. Moreover, the residual noise floor is adaptively chosen based on the structure of the noise power spectral density (NPSD) to further suppress the tonal noise components. Experimental results show the better performance of the proposed algorithm in terms of the segmental signal-to-noise-ratio (SNR) and the PESQ improvements.

On Generalized Auto-Spectral Coherence Function and Its Applications to Signal Detection

Considering that spectral components of one random process are not necessarily independent for all types of signals, this paper defines a generalized auto-spectral coherence function (GAS-CF) to measure this spectral correlation. The GAS-CF is a generalization of the temporal coherence function and the spectral coherence function, where they have already been successfully applied to detect howling components and transient noise components, respectively. After defining the GAS-CF, this paper studies its statistical properties in detail. Simulation results show that the proposed GAS-CF can be applied to detect different types of signals, including transient noise, howling frequency and chirp signal, in a simple way.

Active Headrest with Robust Performance Against Head Movement

In an active noise-reducing headrest with virtual microphones, the noise attenuation achieved at the ears of the listener usually decreases significantly as the head moves away from the central seat position. This paper presents a study on designing an active headrest with robust performance against head movement. To solve this problem, a minimax optimization problem is presented to design appropriate plant models for the system. Experiments are carried out on an active headrest system with the remote microphone technique. Experimental results show that the proposed method can extend the lateral head movement range from 2 cm to about 6 cm, within which the active headrest provides noise attenuation of greater than 10 dB for both ears of the listener, and thus improve the performance robustness of the active headrest system.

A Constrained MMSE LP Residual Estimator for Speech Dereverberation in Noisy Environments

After revealing that both late reverberation and noise are additive interference components in the residual domain, this paper proposes to suppress these additive interference components by using a constrained minimum mean square error linear prediction (LP) residual estimator, where the optimal filter can be obtained by the generalized singular value decomposition. We propose to estimate the LP residuals for both late reverberation and noise continuously, which is based on the non-VAD related noise power spectral density estimator and the incessant late reverberant spectral variance estimator. The non-intrusive objective measure and the PESQ show that the proposed algorithm is better than traditional LP residual-based algorithms and spectral subtraction-based algorithms.

A Statistical Analysis of Two-Channel Post-Filter Estimators in Isotropic Noise Fields

This paper derives explicit expressions of the probability density functions of the two-channel post-filter estimators in isotropic noise fields to study their statistical properties. According to the analysis results, three methods are proposed to improve the performance of the noise filed coherence (NFC)-based post-filter estimator.

Binaural coherent-to-diffuse-ratio estimation for dereverberation using an ITD model

Most previously proposed dual-channel coherent-to-diffuse-ratio (CDR) estimators are based on a free-field model. When used for binaural signals, e.g., for dereverberation in binaural hearing aids, their performance may degrade due to the influence of the head, even when the direction-of-arrival of the desired speaker is exactly known. In this paper, the head shadowing effect is taken into account for CDR estimation by using a simplified model for the frequency-dependent interaural time difference and a model for the binaural coherence of the diffuse noise field. Evaluation of CDR-based dereverberation with measured binaural impulse responses indicates that the proposed binaural CDR estimators can improve PESQ scores.

Robustness analysis of time-domain and frequency-domain adaptive null-forming schemes

For two nearby microphones, adaptive null-forming (ANF) scheme is a simple and effective algorithm for suppressing directional noise sources. In this paper, we analyze the robustness of the time-domain ANF (TD-ANF) and the frequency-domain ANF (FD-ANF) schemes in theory. The analysis reveals several distinctive phenomena. First, the two ANF schemes reduce the amount of interference-plus-noise reduction (INR) and increase the speech distortion (SD) simultaneously as the microphone mismatch increases. Second, the FD-ANF scheme has better performance than the TD-ANF scheme when multiple spatially separated noise sources are W-disjoint orthogonal and located in the back half plane. Third, both the TD-ANF and the FD-ANF schemes are very sensitive to the microphone mismatch at low-frequency bands for most noise scenarios.

Acoustical Vehicle Detection Based on Bispectral Entropy

A vehicle detection algorithm based on Bispectral entropy is proposed in this paper. Based on quadratic-phase coupling(QPC) analysis, Bispectral entropy, as the complexity measure of bispectra, is calculated from the vehicle acoustic signal database which is acquired in several real world experiments. Furthermore, an effective bispectral entropy-based algorithm is developed for vehicle detection. Experiments show that the proposed algorithm can achieve longer distance alert than other two detectors.

Robust Adaptive Beamforming Using Noise Reduction Preprocessing-Based Fully Automatic Diagonal Loading and Steering Vector Estimation

Diagonal loading provides a powerful and effective way to improve the robustness of the standard Capon beamformer. Several parameter-free robust adaptive beamformers (RAB) are considered in this paper. We reveal that the performances of them have somewhat degradation when the number of snapshots or that of sensors is large. To solve this problem, we emphatically study the well-known generalized linear combination-based method, the performance of which may degrade severely when the number of sensors increases, and propose a novel parameter-free technique, which is a combination of noise reduction preprocessing technique and truncated minimum mean square error criterion. As most of the parameter-free RAB techniques are very sensitive to the desired signal steering vector mismatch, this paper further proposes to construct a series connection between these RAB techniques and a steering vector estimation (SVE) method, where the SVE is implemented by a convex optimization technique. Simulation results show that the proposed method can achieve a promising performance in comparison with the competing methods.

Statistical analysis and improvement of coherent-to-diffuse power ratio estimators for dereverberation

Dereverberation methods based on coherent-to-diffuse power ratio (CDR) estimates exploit the spatial properties of signals to suppress late reverberation components. Naturally, the quality of the CDR estimate has a major effect on the quality of the dereverberated signals. This paper presents a statistical study of the performance of state-of-the-art CDR estimators under a Gaussian signal model. The study reveals that the variance of the magnitude of the spatial coherence estimate is the major cause for the degradation of known CDR estimators, while the variance of the phase has only limited impact. Moreover, the variance has more impact on direction-of-arrival (DOA)-independent CDR estimators than on DOA-dependent estimators. Accordingly, we propose a cepstrum thresholding technique to reduce the variance of the spatial coherence estimate. Experimental results show that better performance can be achieved for dereverberation when the proposed spatial coherence method is applied to CDR estimators, especially for the DOA-independent estimators.