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Speech is the main medium for human communication and interaction. Apart from the traditional telephones, more and more applications come with speech interfaces, which use speech signal as an input for various purposes. However, many of these applications might fail to perform in noisy environments as the signal-to-noise ratio (SNR) degrades. Two important measures for any speech enhancement algorithm are noise suppression and speech distortion. Naturally, different speech enhancement algorithms will have different trade-offs. Moreover, depending on the environment, it is possible that one algorithm will outperform the others in some respects. This paper proposes a multi-filter system, which has the capability of continually adjusting the noise suppression level and the speech distortion level in a Pareto fashion. Moreover, we show that the system works under a variety of noisy environments and we obtain the efficient frontier of the combined filters for each background noise. Because the multi-filters are adapting in parallel, the final system can be implemented on FPGA efficiently.

A new class of doubletalk detector based on exploiting a spectral slit is proposed. This is achieved by spectrally deleting a frequency band in the far-end signal such that when the near-end signal is present, only the near-end spectral information is present. The proposed method relies solely on the detection of speech activity period in the slit area, and significantly, it requires no estimation of the echo path. Evaluation in typical acoustic echo setups shows that the proposed method outperforms other conventional doubletalk detectors in terms of probability of miss detection even under poor echo to noise ratio (ENR), low echo to far-end ratio (EFR) conditions and echo path change.

This paper investigates the problem of enhancing a single desired speech source from a mixture of signals in multispeaker environments. A beamformer structure is proposed which combines a fixed beamformer with postfiltering. In the first stage, the fixed multiobjective optimal beamformer is designed to spatially extract the desired source by suppressing all other undesired sources. In the second stage, a multichannel power spectral estimator is proposed and incorporated in the postfilter, thus enabling further suppression capability. The combined scheme exploits both spatial and spectral characteristics of the signals. Two new multichannel spectral estimation methods are proposed for the postfiltering using, respectively, inner product and joint diagonalization. Evaluations using recordings from a real-room environment show that the proposed beamformer offers a good interference suppression level whilst maintaining a low-distortion level of the desired source.

This paper presents an evaluation of a subband beamforming scheme for speech enhancement and acoustic echo suppression applications, such as hands-free telephony, internet telephony and video conferencing. The aim of the paper is to study the impact of the different types of subband filter banks on the speech enhancement performance. Results show that the octave band filter banks provide an effective means to give an overall efficient processing by using a fewer free filter parameters while providing a similar performance in terms of Signal to Noise Interference Ratio (SNIR) as compared to the uniform case. Whilst the critical band filter bank uses the most subbands, it provides the best performance both objectively and subjectively. This study provides insight to the design of filter bank for future hardware implementations. © 2008 IEEE.

Speech signal extraction is becoming more and more important as evidently displayed by its numerous applications such as mobile phones, conference equipment and surveillance. This paper presents a blind method to enhance a speech source of interest in noisy environments. The proposed technique consists of the principal component analysis (PCA) and the independent component analysis (ICA) to extract the speech signal. In an effort to overcome the small phase resolution due to the constraint on the inter-element distance, a non-uniform spacing PCA-ICA algorithm is suggested. By utilizing a different inter-element distance processing on each pair of microphones in a multistage fashion, a better separation is achieved. Results show better separation performance for the proposed method compared to the uniformly spaced microphone array.

This paper introduces a new scheme which combines the popular blind signal separation (BSS) and a post-processor to jointly suppress noise and acoustic echo. The new L element structure uses the BSS as a front-end processor to spatially extract the target signal from the interference (noise and echo). Statistical measures are then employed to select the target signal dominant signal from the BSS outputs. The remaining L − 1 BSS outputs (noise and echo dominant) and the existing far-end line echo are then used as the reference signals in an adaptive noise canceller (ANC) to temporally enhance the target signal. The novel structure bypasses the need for any a priori information whilst compensating the separation quality of the BSS temporally. Real room evaluations demonstrate the efficacy of the scheme in both noisy double-talk and non-double-talk situation.

This paper introduces a new subband adaptive space constrained beamforming structure for use in hands-free speech enhancement applications. The scheme incorporates a space constrained source model and voice activity information through the integration of a voice activity detector (VAD). The VAD information is used to estimate noise covariance information during non-speech periods and to optimally estimate the source power spectral density (PSD), which is used to provide a spectrally optimized constraint on the source. The proposed structure is evaluated in a real car environment, yielding results which compare well to the optimal Wiener solution where full knowledge of the source is known.

In this paper, we introduce and investigate a method combining principle component analysis (PCA) and independent component analysis (ICA) for blind source separation (BSS). A recursive method for the PCA is applied to meet the demands of a real-time application, and likewise a modified on-line version of the information maximization principle is used. The combined PCA-ICA algorithm, first reduces the dimension of the problem and then separates the signals. Evaluation of the proposed algorithm in a real room shows superior noise suppression capabilities compared to the use of PCA or ICA individually. The proposed algorithm achieves an impressive noise suppression/separation of up to 14 dB with only two microphones. Most importantly this is achieved with negligible distortion of the recovered signal. © 2005 IEEE.

This paper introduces a new subband adaptive space constrained beamforming structure for use in hands-free speech enhancement applications. The scheme incorporates a space constrained source model and voice activity information through the integration of a voice activity detector (VAD). The VAD information is used to estimate noise covariance information during non-speech periods and to optimally estimate the source power spectral density (PSD), which is used to provide a spectrally optimized constraint on the source. The proposed structure is evaluated in a real car environment, yielding results which compare well to the optimal Wiener solution where full knowledge of the source is known. ©2005 IEEE.

This paper presents a new robust microphone array processing technique to enhance speech signal under the influence of noise and jammer(s). The new structure comprises of two soft-constrained subband beamformers and a non-coherent processing technique.Essentially the first beamformer enhances the desired speech signal in a specific constrained region. The residual interference in the beamformer's output is then spectral subtracted using the estimated interference from the secong beamformer. Evaluations in a real office environment show higher interference suppression compared to those obtained using the soft constrained beamformer only. Most importantly, this is achieved with negligible expense on target signal distortion.

This paper presents a new robust microphone array processing technique to enhance speech signals under the influence of noise and jammer(s). The new structure comprises two soft constrained subband beamformers and a non-coherent processing technique. Essentially, the first beamformer enhances the desired speech signal in a specified constrained region. The residual interference in the beamformer's output is then spectral subtracted using the estimated interference from the second beamformer. Evaluations in a real office environment show higher interference suppression compared to those obtained using the soft constrained beamformer only. Most importantly, this is achieved with negligible expense on target signal distortion.

This paper presents a new robust microphone array to enhance speech signal under the influence of noise and jammer(s). The proposed structure comprises of a soft con-strained subband beamformer, a blocking system and a non-coherent processing technique. The soft constrained beamformer enhances the desired speech signal in a specified region by suppressing all side-lobes. This enhanced signal is then used as a reference signal by the blocking system to estimate the interference spectrum. Finally, the beamformer’s output is spectral subtracted using the estimated interference spectrum. Simulations in a real office environment show higher interference suppression level compared to those obtained using the soft constrained beamformer only. Most importantly, this is achieved with negligible expense on target signal distortion.