Hongsen He

An Active Impulsive Noise Control Algorithm With Logarithmic Transformation

To overcome the limitations of the existing algorithms for active impulsive noise control, an algorithm based on minimizing the squared logarithmic transformation of the error signal is proposed in this correspondence. The proposed algorithm is more robust for impulsive noise control and does not need the parameter selection and thresholds estimation according to the noise characteristics. These are verified by theoretical analysis and numerical simulations.

Time Difference of Arrival Estimation Exploiting Multichannel Spatio-Temporal Prediction

To localize sound sources in room acoustic environments, time differences of arrival (TDOA) between two or more microphone signals must be determined. This problem is often referred to as time delay estimation (TDE). The multichannel cross-correlation-coefficient (MCCC) algorithm, which is an extension of the traditional cross-correlation method from two- to multiple-channel cases, exploits spatial information among multiple microphones to improve the robustness of TDE. In this paper, we propose a multichannel spatio-temporal prediction (MCSTP) algorithm, which can be viewed as a generalization of the MCCC principle from using only spatial information to using both spatial and temporal information. A recursive version of this new algorithm is then developed, which can achieve similar performance as MCSTP, but is computationally more efficient. Experimental results in reverberant and noisy environments demonstrate the advantages of this new method for TDE.

Robust blind identification of room acoustic channels in symmetric alpha-stable distributed noise environments

Blind multichannel identification is generally sensitive to background noise. Although there have been some efforts in the literature devoted to improving the robustness of blind multichannel identification with respect to noise, most of those works assume that the noise is Gaussian distributed, which is often not valid in real room acoustic environments. This paper deals with the more practical scenario where the noise is not Gaussian. To improve the robustness of blind multichannel identification to non-Gaussian noise, a robust normalized multichannel frequency-domain least-mean M-estimate algorithm is developed. Unlike the traditional approaches that use the squared error as the cost function, the proposed algorithm uses an M-estimator to form the cost function, which is shown to be immune to non-Gaussian noise with a symmetric α-stable distribution. Experiments based on the identification of a single-input/multiple-output acoustic system demonstrate the robustness of the proposed algorithm.

Multichannel time delay estimation for acoustic source localization via robust adaptive blind system identification

In the problem of acoustic source localization, time difference of arrival (TDOA) among multiple sensors is needed, which is often obtained through time delay estimation (TDE) techniques. Among the multiple TDE methods developed in the literature, the normalized multichannel frequency-domain least-mean-square (NMCFLMS) algorithm is shown robust to reverberation. The performance of this algorithm, however, deteriorates in non-Gaussian and low signal-to-noise ratio (SNR) Gaussian noise environments. In this paper, we re-derive a robust normalized multichannel frequency-domain least-mean-M-estimate (RNMCFLMM) algorithm to estimate TDOAs for acoustic source localization. The proposed algorithm exploits the non-sensitivity of an M-estimator to non-Gaussian noise and makes a tradeoff between the least-squares and least-absolute criteria to improve the robustness of TDE with respect to non-Gaussian and Gaussian noises. The effectiveness of the proposed algorithm is demonstrated in real acoustic environments.

On time delay estimation from a sparse linear prediction perspective

This paper proposes a sparse linear prediction based algorithm to estimate time difference of arrival. This algorithm unifies the cross correlation method without prewhitening and that with prewhitening via an ℓ2/ℓ1 optimization process, which is solved by an augmented Lagrangian alternating direction method. It also forms a set of time delay estimators that make a tradeoff between prewhitening and non-prewhitening through adjusting a regularization parameter. The effectiveness of the proposed algorithm is demonstrated in noisy and reverberant environments.

A steered response power approach with trade-off prewhitening for acoustic source localization

This paper proposes a steered response power (SRP) approach with trade-off prewhitening to acoustic source localization. To obtain effective compromise prefiltering of microphone signals, the sparsity of speech amplitude spectrum is used to establish a convex-constraint linear prediction model, which is solved by a split Bregman method. The presented approach unifies the traditional SRP and steered response power via phase transform prefiltering methods and achieves a good compromise between them from the perspective of localization performance. The superiority of the proposed method is demonstrated in noisy and reverberant environments.

Robust multichannel TDOA estimation for speaker localization using the impulsive characteristics of speech spectrum

Time delay estimation (TDE) plays an important role in localizing and tracking radiating acoustic sources. Although many efforts have been devoted to this problem in the literature, the robustness of TDE with respect to noise and reverberation remains a great challenge for practical systems. In this paper, we investigate the TDE problem in acoustic single-input/multiple-output (SIMO) systems in reverberant and noisy environments. We first define a Cauchy estimator in the frequency domain, which is robust in dealing with speech as the SIMO systems excitation. This robust estimator is then used to construct a cost function, from which a robust multichannel frequency-domain adaptive filter is deduced. This adaptive algorithm is subsequently employed to blindly identify the acoustic impulse responses between the source and the microphones. Finally, the time difference of arrival is determined from the identified channel responses.

On directivity of a circular array with directional microphones

Circular microphone arrays have a broad range of applications in teleconferencing and hands-free telecommunication systems. Directional microphones are extensively used to construct the circular arrays to obtain superior sound quality. However, the 360-degree coverage ability of this type of circular arrays is seldom investigated. In this paper, we develop a circular array with the use of four firstorder supercardioid microphones to obtain 360-degree coverage of sound recording in teleconferencing environments. Through analyzing the directional response of this array, we derive an optimal range of the array radius for uniform 360-degree coverage of sound capturing. Experiments are carried out in an anechoic chamber to test the performance of the developed microphone array.

On time delay estimation based on multichannel spatiotemporal sparse linear prediction

Noise and reverberation can significantly affect the performance of time delay estimation (TDE) in room acoustic environments. The multichannel cross-correlation coefficient (MCCC) algorithm, which extends the traditional cross-correlation method from two to multiple channels, can exploit the spatial information among multiple microphones to improve the robustness of TDE with respect to environmental noise; but this algorithm is not robust to reverberation. The multichannel spatiotemporal prediction (MCSTP) algorithm uses both the spatial and temporal information provided by the array. This algorithm improves significantly the robustness of TDE with respect to reverberation; however, it is found sensitive to noise. In this paper, we develop a multichannel spatiotemporal sparse prediction (MCSTSP) algorithm for TDE. This algorithm obtains a good compromise between robustness of TDE to noise and that to reverberation through making a tradeoff between pre-whitening and non-prewhitening. This is achieved via adjusting a regularization parameter, which is solved by an augmented Lagrangian alternating direction method of multipliers (ADMM). The property of this developed algorithm is justified with numerical experiments in both noisy and reverberant environments.