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This letter presents a new computational procedure for the second-order gradient-based blind signal separation (BSS) problem with convolutive mixtures that has improved convergence characteristics over the steepest descent algorithm. The BSS problem is formulated as a constrained optimization problem with complex unmixing weight matrices where the constraints are formulated to overcome the permutation effects. This problem is then transformed into an unconstrained optimization problem, so that the conjugate gradient algorithm can be applied. The convergence of the proposed procedure is compared with the steepest descent algorithms in real and simulated environments.

Orthogonal frequency-division multiplexing (OFDM) technique has become a popular choice for underwater acoustic (UA) networks recently. Impulsive noise is one of the factors that limit the performance of this technique, and the mitigation of impulsive noise receives increasing attention in the UA communication community. In this paper, a pilot-based algorithm is proposed to mitigate the impact of impulsive noise. The proposed algorithm introduces a special OFDM block as preamble during which impulsive noise mitigation is performed using the null subcarriers and then the channel response is estimated using pilot subcarriers. By assuming that the channel is quasi-stationary, the proposed algorithm adopts the channel estimation result of the previous block to estimate and mitigate the impulsive noise of the current block. We apply the proposed algorithm to process the data collected during the experiment conducted in December 2015, in the estuary of the Swan River, Western Australia. The results show that the proposed approach is able to mitigate the impulsive noise for UA OFDM systems.

The limiting feature of hearing aids is acoustic feedback. This feedback problem has been approached by using an acoustic feedback canceller. This well known identification in a loop problem, will give a biased solution due to correlation between the desired and loudspeaker signals. Speech and music signals have long tails in the correlation. As a result, the performance of the system is considerably degraded and under certain conditions the cancellation system will be unstable. The two-microphone techniques have the potential to significantly reduce this problem. This paper introduces the applications of uniform and non-uniform sub-band techniques into the two-microphone acoustic feedback cancellation (AFC-2mics) to decorrelate input signals and individually adapt solutions in those bands. The system has been evaluated in terms of Misalignment (MisAL) and Maximum Stable Gain (MSG) using both male and female speech input signals. The simulation results show that our proposed methods provide better performance than the other existing methods.

Underwater acoustic (UA) communication channel is extremely bandlimited and fast time-varying. Block-by-block decision-directed channel tracking is a bandwidth efficient channel estimation method for pilot-aided single-carrier UA communication systems. However, conventional forward-only decision-directed method suffers from error propagation where the system bit-error-rate (BER) increases with the interval between the pilot sequence and data symbols. To mitigate the error propagation effect, in this paper, a backward decision-directed algorithm is proposed to detect data blocks which are at large internal from the previous pilot sequence but close to the next one. The proposed algorithm is applied to detect data received during the experiment conducted in December 2012 in the Indian Ocean off the Rottnest Island, Western Australia. The results show that using the proposed algorithm, the average uncoded BER of the last data block in one frame is much lower than the BER yielded by the forward-only tracking method and is only sightly higher than the BER of the first data block.

In this paper, we study the transceiver design problem for amplify-and-forward interference multiple-input multiple-output (MIMO) relay communication systems, where multiple transmitter-receiver pairs communicate simultaneously with the aid of a relay node. We aim at minimizing the mean-squared error (MSE) of the signal waveform estimation at the receivers subjecting to transmission power constraints at the transmitters and the relay node. Since the transceiver optimization problem is nonconvex with matrix variables, the globally optimal solution is intractable to obtain. To overcome the challenge, we propose an iterative transceiver design algorithm where the transmitter, relay, and receiver matrices are optimized iteratively by exploiting the optimal structure of the relay precoding matrix. Simulation results show that the proposed algorithm performs better than the existing technique in terms of both MSE and bit-error-rate.

In this paper we propose the use of the proportionate principle in order to improve the convergence characteristics of the two microphone method for acoustic feedback cancellation in hearing aids. The reason of using proportionate algorithms is to exploit the sparseness of the adaptive filter coefficients in the transform domain. The convergence improvement can be achieved for both speech and music signals at a moderate increase of the numerical complexity over that of a previous solution in the transform domain.

This paper presents a design of microphone array system that is intended for conference room applications. Different microphone array configurations have been considered in this work to demonstrate the importance of array layout. The filter-and-sum broadband beamformer is designed by using weighted least square (WLS) optimization method. As existing near-field broadband beamformers are sensitive to changes in microphone characteristics and position, robust designs need to be considered that include microphone characteristics mismatch (gain and phase) in the design. Furthermore, it is shown that the aperture size is important for the designed beamformer performance. Golden section search technique is used to find optimal aperture size and weights. The important roles that microphone array layout, robust design and aperture size play in the design of the broadband beamformer are demonstrated.

Affine projection algorithms (APA) have been widely employed for acoustic echo cancellation (AEC) since they provide a natural trade-off between convergence speed and computational complexity. However, their application in Acoustic Feedback Cancellation (AFC) in hearing aids so far has been limited due to lack of performance improvement in one microphone settings. A two microphone technique was recently proposed to provide an improved cancellation for a larger class of input signals. This paper proposes to use APA in the two microphone closed-loop feedback cancellation. It is shown that APA has significantly improved the misalignment and maximum stable gain of the system. Moreover, a new variable Gaussian step-size control (VGSS) for APA is also proposed. The simulation results indicate an improvement in convergence speed of the proposed algorithms as compared to previously suggested methods.

In industrial noise environments, the use of assistive listening headsets is a means to provide adequate access to voice communication while wearing hearing protection. This paper presents a performance evaluation and comparison of two different methods to provide the binaural speech enhancement in real industrial noise scenarios. The investigated binaural methods based on differential beamforming and multichannel Wiener filter show different strengths and weaknesses. A transient noise suppression algorithm is also proposed and evaluated. Performance evaluation shows that this algorithm, together with the binaural multi-channel Wiener filter approach, can successfully reduce the hammering noise. This can be observed from the PESQ scores and the signal characteristics.

In MIMO applications, the minimum mean square error parallel interference cancellation (MMSE-PIC) based Soft-Input Soft-Output (SISO) detector has been widely adopted because of its low complexity and good bit error rate (BER) performance. In this paper, we firstly propose to use a Gaussian model based MMSE detection algorithm to implement MMSE-PIC with low complexity. This algorithm, which can detect a length-N r received data block by a single Hermitian matrix (sized N t × N t ) inversion, is especially preferable in Massive MIMO up-link applications where the number of transmit antennas N t from each end terminal is much less than the number of receive antennas N r in the Base Station. Then we derive a new method to calculate the matrix inversion by a linear combination of two matrices, which reduces the complexity from O(N t 3) to O(N t 2). At last, in order to improve the system performance for the first pass when there is no a priori information available, a self-iteration method is proposed and thus a system performance gain of 1dB to 2dB is achieved at the cost of modest complexity increase.

In this paper, we formulate a general design of transversal filter structures with maximum relative passband-to-stopband energy ratio subject to complex frequency response constraints in the passband and the stopband as well as additional constraints such as null constraints. These null constraints are important for applications where the suppression of noise at certain frequencies are important. Additional constraints are introduced allowing approximately linear phase and constant group delay in the passband. For a given set of basis functions, the design problem can be formulated as a semi-infinite quadratic optimization problem in the filter coefficients, which are the decision variables to be optimized. In this paper, we focus on the design of digital Laguerre filter and digital finite impulse response (FIR) filter structures. A modified bridging algorithm is developed for searching for the optimum pole of the Laguerre filters. Design examples are given to demonstrate the effectiveness of the proposed algorithm.

Traditionally, voice activity detection algorithms are based on any combination of general speech properties such as temporal energy variations, periodicity, and spectrum. This paper describes a novel statistical method for voice activity detection using a signal-to-noise ratio measure. The method employs a low-variance spectrum estimate and determines an optimal threshold based on the estimated noise statistics. A possible implementation is presented and evaluated over a large test set and compared to current modern standardized algorithms. The evaluations indicate promising results with the proposed scheme being comparable or favorable over the whole test set.

In this article, we consider the practical application of the dual parametrization method for semi-infinite quadratic optimization problems. It is known that the efficiency of the dual parametrization algorithm depends on the method used in its final local search step. For some practical problems, the variables representing the index points in the parametrized dual problem may be sensitive and available local search methods may have difficulty finding the precise optimal locations of the index positions. Thus, we propose modifications to the dual parametrization algorithm developed earlier to overcome these problems. The modified algorithm is applied to a general design of antenna arrays. Numerical results are compared with other design methods. ¶Dedicated to N.U. Ahmed on the occassion of his 70th birthday.

Multirate adaptive filters have numerous advantages such as low computational load, fast convergence, and parallelism in the adaptation. Drawbacks when using multirate processing are mainly related to aliasing and reconstruction effects. These effects can be minimized by introducing appropriate problem formulation and employing sophisticated optimization techniques. In this paper, we propose a formulation for the design of a filter bank which controls the distortion level for each frequency component directly and minimizes the inband aliasing and the residual aliasing between different subbands. The advantage of this problem formulation is that the distortion level can be weighted for each frequency depending on the particular practical application. A new iterative algorithm is proposed to optimize simultaneously over both the analysis and the synthesis filter banks. This algorithm is shown to have a unique solution for each iteration. For a fixed distortion level, the proposed algorithm yields a significant reduction in both the inband aliasing and the residual aliasing levels compared to existing methods applied to the numerical examples.

Subband adaptive filters have been proposed to avoid the drawbacks of slow convergence and high computational complexity associated with time domain adaptive filters. However, subband processing causes signal degradations due to aliasing effects and amplitude distortions. This problem is unavoidable due to further filtering operations in subbands. In this letter, the problems of aliasing effect and amplitude distortion are studied. Prototype filters which are optimized with respect to those properties are designed and their performances are compared. Moreover, the effect of the number of subbands, the oversampling factors and the length of the prototype filter are also studied. Using the multicriteria formulation, all Pareto optimums are sought via the nonlinear programming technique. We find that the prototype filter designed via the Kaiser window provides the best overall performance among the methods we studied. Also, there is a critical oversampling factor beyond which the improvement of performance is diminishing. Finally, if the length of the prototype filter increases with the number of subbands, an increase in the number of subbands will not deteriorate the performance.

Broadband microphone arrays has important applications such as hands-free mobile telephony, voice interface to personal computers and video conference equipment. This problem can be tackled in different ways. In this paper, a general broadband beamformer design problem is considered. The problem is posed as a Chebyshev minimax problem. Using the l/sub 1/-norm measure or the real rotation theorem, we show that it can be converted into a semi-infinite linear programming problem. A numerical scheme using a set of adaptive grids is applied. The scheme is proven to be convergent when a certain grid refinement is used. The method can be applied to the design of multidimensional digital finite-impulse response (FIR) filters with arbitrarily specified amplitude and phase.

The letter provides an analysis of optimal finite-impulse response subband beamforming for speech enhancement in multipath environments. A modification of the direct-path standard Wiener formulation is shown to give nearly optimal performance when it comes to signal-to-noise-plus-interference ratio, by including coherent multipath propagation in the criterion.

This paper analyzes subband beamforming schemes mainly aimed at speech enhancement and acoustic echo suppression applications such as hands-free telephony for both mobile and office environments, Internet telephony and video conferencing. Analytical descriptions of both causal finite-length and noncausal infinite-length subband microphone array structures are given. More specifically, this paper compares finite Wiener filter performance with the noncausal Wiener solution, giving a comprehensive theoretical suppression limit. It is shown that even short filters will yield a good approximation of the infinite solution, provided that the element spacing and temporal sampling is matched to the frequency band of interest. Typically, 10-20 FIR taps are sufficient in each subband.

In everyday environments, we are frequently immersed by unwanted acoustic noise and interference while we want to listen to acoustic signals, most often speech. Technology for assisted listening is then desired to increase the efficiency of speech communication, reduce listener fatigue, or just allow for enjoying undisturbed sounds (e.g., music). For people with normal hearing, assisted listening devices (ALDs) mainly aim to achieve hearing protection or increase listening comfort; however, for hearing-impaired individuals, as the most prominent user group so far, further progress of assisted listening technology is crucial for better inclusion into our world of pervasive acoustic communication.

In this paper, after showing MMSE-SIC suffers from performance loss when the channel is spatially correlated for Massive MIMO, we propose an effective hybrid iterative detection algorithm named partial Gaussian approach with integer programming (PGA-IP) to handle correlated channels. In PGA-IP, a partial gaussian approach is first employed to reduce the massive MIMO detection (with large dimension N t ×N r MIMO channel) to a problem of marginalizing M (M is a parameter and M≪ N t , N r ) discrete valued symbols over an M-degree quadratic function. Then we employ integer programming which is a tree based branch-and-bound search algorithm to further reduce the complexity of the M-dimensional marginalization. Simulation results show that the proposed PGA-IP outperforms MMSE-SIC by about 5dB under heavily correlated channel with only several times of increased computational complexity. At the same time, with about 5% of the complexity of the exact PGA algorithm, the proposed PGA-IP only suffers marginal performance penalty.

This paper presents the gain saturation characteristics for different input pulse shapes in semiconductor optical amplifiers (SOAs). Finite-difference beam propagation method (FD-BPM) is used for simulation and compared the gain saturation characteristics in SOA for different pulse shapes.

In this paper we are studying the use of two microphones for acoustic feedback cancellation in hearing aids. With the two microphones approach, an additional microphone is employed to provide added information about the signals which is then utilized to obtain an incoming signal estimate. This estimate is removed from the error signal prior to adapting the canceler, thus removing the undesired signal correlation. In this paper, we propose to use orthogonal transforms with the two microphones approach. The discrete Fourier transform and the discrete cosine transform are implemented to transform the adaptive filter signals. Also, a bank of adaptive filters is employed, each adapting to different portions of the spectrum for a finer control of the adaptation process. Simulation results based on real measured feedback paths and speech signals show improved convergence rates and stable solutions. Index Terms—Acoustic feedback, bias problem, feedback cancellation, hearing aids, two microphones

We introduce a novel approach for source number estimation through an adaptive fuzzy c-means clustering. Spatial feature vectors are extracted from microphone observations, weighted for reliability and then clustered in a full-band manner using an adaptive variation on the fuzzyc-means. A number of quality measures are combined to produce a weighted sum which is used to find the optimal number of clusters at each iteration of the clustering algorithm. Experimental evaluations using real-world recordings from a reverberant room (RT60 = 390 ms) demonstrated encouraging performance in both even- and under-determined conditions.

This paper discusses a multirate filterbank-based extended infomax algorithm for real-world signal separation, i.e., convolved mixtures separation. Since convolution in the time domain corresponds to instantaneous mixing in the frequency domain, polyphase subband projection naturally becomes an efficient alternative to the Fourier transform based frequency domain approach. The online implementation proposed is featured by a simultaneous inverse channel identification in the frequency domain and signal filtering in the time domain. It is shown that an over-representation structure reduces aliasing between different bands and results in more accurate inverse channel estimates. Therefore, it provides better performance than the Fourier transform based structure in the measures of both separation and distortion. The performance limitation of the method is also evaluated in terms of the Wiener solution.

In hands-free speech communication, the signal-to-noise ratio (SNR) is often poor, which makes it difficult to have a relaxed conversation. By using noise suppression, the conversation quality can be improved. This paper describes a noise suppression algorithm based on spectral subtraction. The method employs a noise and speech-dependent gain function for each frequency component. Proper measures have been taken to obtain a corresponding causal filter and also to ensure that the circular convolution originating from fast Fourier transform (FFT) filtering yields a truly linear filtering. A novel method that uses spectrum-dependent adaptive averaging to decrease the variance of the gain function is also presented. The results show a 10-dB background noise reduction for all input SNR situations tested in the range -6 to 16 dB, as well as improvement in speech quality and reduction of noise artifacts as compared with conventional spectral subtraction methods.

The growth of wireless communication continues. There is a demand for more user capacity from new subscribers and new services such as wireless internet. In order to meet these expectations new and improved technology must be developed. A way to increase the capacity of an existing mobile radio network is to exploit the spatial domain in an efficient way. An antenna array adds spatial domain selectivity in order to improve the Carrier-to-Interference ratio (C/I) as well as Signal-to-Noise Ratio (SNR). An adaptive antenna array can further improve the Carrier-to-Interference ratio (C/I) by suppressing interfering signals and steer a beam towards the user. The suggested scheme is a combination of a beamformer and an interference canceller. The proposed structure is a circular array consisting of K omni-directional elements and combines fixed beamforming with interference cancelling. The fixed beamformers use a weight matrix to form multiple beams. The interference cancelling stage suppresses undesired signals, leaking into the desired beam. The desired signal is filtered out by the fixed beamforming structure. Due to the side-lobes, interfering signals will also be present in this beam. Two alternative strategies were chosen to cancel these interferers; use the other beamformer outputs as inputs to an adaptive interference canceller; or regenerate the outputs from the other beamformer outputs and generate clean signals which are used as inputs to adaptive interference cancellers. Resulting beamformer patterns as well as interference cancellation simulation results are presented. Two different methods have been used to design the beamformer weights, Least Square (LS) and minimax optimisation. In the minimax optimisation a semi-infinite linear programming approach was used. Although the optimisation plays an essential role in the performance of the beamformer, this paper is focused on the application rather then the optimisation methods.

In this letter, an enhanced verification-based decoding algorithm (EVA) for packet level low-density parity-check (LDPC) codes is proposed. Compared with the verification algorithm (VA) in the literature, the proposed algorithm reduces the likelihood of false verification by enhancing the verification condition to achieve a better decoding performance. For example, simulation results in this letter show that EVA reduces frame error rate (FER) by two orders when comparing the VA over binary symmetric channel (BSC), while the increase of the computational load is less than 90%.

The articles in this special section focus on the signal processing technologies and applications that support assisted listening.

Broadband beamforming techniques are of importance in several applications such as microphone arrays, sonar arrays, and radio astronomy and broadband radar. The capability to obtain a good broadband response over a wide range of frequencies is important to maintain good signal integrity and spatial selectivity. Since the beamformer needs to operate over several octaves, there are a number of important considerations in the design, such as beamformer beamwidth in the low frequency range and sensor element placement. The beamformer design can be performed directly in the wave domain. However, the more common approach is to design the beamformer in the spatially sampled domain. Another way to design the beamformer weights is according to the presented data, which gives an optimal beamformer. Other important aspects of beamforming are the capability to steer the beam towards a specific input signal and also capability to focus the beamformer in the near field.

This paper presents an adaptive coherence-based beamforming algorithm that enhances a target speech signal from an arbitrary direction of arrival (DOA) in the azimuthal plane, while localising and suppressing the interfering speech signal and noise from other directions. In these complex noisy acoustic environments, spatially selective speech enhancement schemes provide significant advantages over single channel enhancement methods. Experimental results demonstrate that the proposed method achieves considerable improvement in Perceptual Evaluation of Speech Quality (PESQ). Moreover, the proposed algorithm is directly suited for real-time implementation.

This paper investigates the problem of subband speech separation from a mixture of two speech signals in a room environment. Due to the lack of source information, a sound source localization is proposed for beamformer design in each subband to obtain a good interference suppression with negligible expense on each target signal integrity. Evaluations on real speech data show that the proposed speech separation method offers a good interference suppression level whilst maintaining a low distortion level of the target source.

In OFDM systems, the cyclic prefix (CP) eliminates the interblock interference and enables the use of the computationally efficient single-tap equalizers at the receiver. While posing a loss in both spectrum efficiency and power efficiency, the CP brings extra information about the data which can be used for detection. Therefore, instead of discarding the CP as in the conventional OFDM systems, this paper takes advantage of the prefix redundancy and utilizes it in the soft-input soft-output equalizer of a turbo equalization system. By using factor graph, an equalization algorithm is developed, and with proper approximation, the complexity of the proposed algorithm is reduced to O(2Rlog2N + 4RG/N log2G + 2RG/N) per data symbol for R iterations, where N is the length of the block and G is equal to P + L - 1 with P the CP length and L the channel length. Simulation results show that the turbo equalization system converges within two iterations and the proposed equalization approach achieves significant gain compared to the conventional approach.

The real-time implementation of the existing multi-channel Wiener filter (MWF) algorithms suffer from performance degradation due to the lack of robustness against estimation errors of the second-order statistics. The reasons are twofold: one, the estimation of the statistics relies on real voice activity detector (VAD), which often fails in adverse environments. Second, the MWF solutions involve estimation of the second order clean speech statistics, which also exaggerates the errors. This paper presents an MWF algorithm that requires neither VAD nor clean speech statistics. Performance evaluation under real scenarios shows that the proposed method outperforms the conventional MWF solution in terms of the trade-off between noise reduction and speech distortion.

This paper investigates the MMSE-based frequency domain equalization (FDE) algorithms in turbo equalization systems. As opposed to the conventional FDE systems where the cyclic prefix (CP) is discarded at the receiver, we take advantage of the redundancy and make use of all the observed signals for equalization purpose. First, we interpret the conventional frequency domain equalizer as a Forney-style factor graph (FFG), and accordingly an equalization algorithm is derived based on the Gaussian message passing (GMP) technique. Second, the normally discarded CP part is presented similarly using an FFG, and an algorithm that integrates both of the FFGs is proposed. As a result, two extrinsic messages about the data symbols are obtained rather than one, and they are merged together based on a symbol-wise combination. Third, with approximations made on two covariance matrices, the complexity of the proposed equalization algorithm is maintained at the same order as that of the conventional FDE algorithm, i.e. O(NIog2N) per block per iteration. Simulations results verify that, a gain of around 0.7dB is achieved compared with the conventional algorithm at 1/4 CP ratio, for both 16QAM and 64QAM system with Gray mapping over AWGN or ISI channels.

Speech recognition has been used in various real-world applications such as automotive control, electronic toys, electronic appliances etc. In many applications involved speech control functions, a commercial speech recognizer is used to identify the speech commands voiced out by the users and the recognized command is used to perform appropriate operations. However, users' commands are often corrupted by surrounding ambient noise. It decreases the effectiveness of speech recognition in order to implement the commands accurately. This paper proposes a multichannel filter to enhance noisy speech commands, in order to improve accuracy of commercial speech recognizers which work under noisy environment. An innovative particle swarm optimization (PSO) is proposed to optimize the parameters of the multichannel filter which intends to improve accuracy of the commercial speech recognizer working under noisy environment. The effectiveness of the multichannel filter was evaluated by interacting with a commercial speech recognizer, which was worked in a warehouse.

The successful application of automatic speech recognition systems in the real world is conditional on its ability to handle realistic environments with unfavorable conditions such as reverberation and multiple sources of inteference. Previous research has identified time-frequency masking based approaches to blind source separation as a viable approach for multisource reverberant source separation. It is proposed the use of such separation techniques as a front-end to speech recognition will encourage greater recognition accuracy. Experimental evaluations confirmed the hypothesis with an improvement in recognition accuracy of over 20% at a reverberation time of RT 60 = 300ms; this is indicative of the potential for future research in this field.

In an MMSE based turbo equalization system, a soft mapper calculates the symbol mean and variance based on extrinsic Log-Likelihood-Ratios (LLRs) information coming from a Soft-Input Soft-Output (SISO) decoder. In this paper, we investigate the complexity of this module, and in particular, we employ a 3-segment linear approximation approach to calculate the mean and variance of data symbols from LLRs. For FPGA and VLSI implementation, we propose novel piecewise linear functions which map LLR to the mean and variance directly without the use of any two-variable-input multipliers. Simulation results for 16-QAM and 64-QAM show that the no multiplier approach has close BER performance to the 3-segment linear approximation approach with multipliers.

The ability of the human cognitive system to distinguish between multiple, simultaneously active sources of sound is a remarkable quality that is often taken for granted. This capability has been studied extensively within the speech processing community andmany an endeavor at imitation has beenmade. However, automatic speech processing systems are yet to perform at a level akin to human proficiency (Lippmann, 1997) and are thus frequently faced with the quintessential "cocktail party problem": the inadequacy in the processing of the target speaker/s when there are multiple speakers in the scene (Cherry, 1953). The implementation of a source separation algorithm can improve the performance of such systems. Source separation is the recovery of the original sources from a set of observations; if no a priori information of the original sources and/or mixing process is available, it is termed blind source separation (BSS). Rather than rely on the availability of a priori information of the acoustic scene, BSS methods often employ an assumption on the constituent source signals, and/or an exploitation of the spatial diversity obtained through a microphone array. BSS has many important applications in both the audio and biosignal disciplines, including medical imaging and communication systems.

In this paper, a modified a priori SNR estimator is proposed for speech enhancement. The well-known decision-directed (DD) approach is modified by matching each gain function with the noisy speech spectrum at current frame rather than the previous one. The proposed algorithm eliminates the speech transient distortion and reduces the impact from the choice of the gain function towards the level of smoothing in the SNR estimate. An objective evaluation metric is employed to measure the trade-off between musical noise, noise reduction and speech distortion. Performance is evaluated and compared between a modified sigmoid gain function, the state-of-the-art log-spectral amplitude estimator and the Wiener filter. Simulation results show that the modified DD approach performs better in terms of the trade-off evaluation.

The method proposed in this paper improves the identification and cancellation of the feedback path by making the adaptive canceller robust against the impact of the desired speech signal. The proposed method allows for the canceller's coefficients to be continuously adapted allowing it to track variations in the feedback path even in the presence of the desired signal. It suggests the use of dual microphones and dual adaptive filters arranged in such a way that allows the speech signal to be identified and removed from the adaptation process. This results in a more robust solution which was verified by our experiments and evaluations. The perceptual evaluation of speech quality (PESQ) measure was also used to show that the proposed method results in better signal quality.

A new design method for broadband microphone arrays is presented. Using sequences of calibration signals, the method is able to design finite-impulse response (FIR) filters with specific performance. The method can control and adjust the speech distortion, noise suppression, and echo cancellation directly. It turns out that a significantly shorter filter length can be applied to achieve better overall performance than the least-squares method or the signal-to-noise plus interference method.

Publication in the conference proceedings of EUSIPCO, Barcelona, Spain, 2011

Parametric Wiener formulation in speech applications provides an engineering trade-off between noise suppression and target signal distortion levels. However, the tuning parameter is typically difficult to tune as there is a lack of insight into this parameter. Thus far, there is no direct and simple relationship between the tuning parameter and the target distortion constrained Wiener filter. This paper aims to fill this blank by providing a detailed derivation on the upper bound of the tuner to provide further insights on its selection. It is shown that the tuning parameter is bounded by a function of the signal to noise ratio (SNR) and the number of microphones under incoherent and diffuse noise. Further, a simple recursive estimation method is proposed for the estimation of the target signal and noise covariance information.

During the last decade microphone array processing has emerged as a powerful tool for increasing the noise robustness of automatic speech recognition (ASR) systems. Typically,microphone arrays are used as preprocessors that enhance the incoming speech signal prior to recognition. While such traditional approaches can lead to good results, they usually require large numbers of microphones to reach acceptable performance in practice. Furthermore, important information, such as uncertainty estimates and energy bounds, are often ignored as speech recognition is conventionally performed only on the enhanced output of the array. Using the probabilistic concept of evidence modeling this chapter presents a novel approach to robust ASR that aims for closer integration of microphone array processing and missing data speech recognition in reverberant multi-speaker environments. The output of the array is used to estimate the probability density function (pdf) of the hidden clean speech data using any information which may be available before and after array processing. The chapter discusses different types of evidence pdfs and shows how these models can be used effectively during HMM decoding.

Handsfree speaker input of mobile telephones is most desirable today in order to enable safe operation in cars. This is also a prerequisite for voice control of car devices. This paper presents an "on the road" evaluation of the performance of an adaptive microphone array for speech enhancement in cars. The results show a signal-to-noise ratio (SNR) improvement in the range of 10-15 dB over the telephone frequency range with a slight favour for higher frequencies when driving at normal speeds. >