Spatial Noise-Field Control With Online Secondary Path Modeling: A Wave-Domain Approach

Due to strong interchannel interference in multichannel active noise control (ANC), there are fundamental problems associated with the filter adaptation and online secondary path modeling remains a major challenge. This paper proposes a wave-domain adaptation algorithm for multichannel ANC with online secondary path modelling to cancel tonal noise over an extended region of two-dimensional plane in a reverberant room. The design is based on exploiting the diagonal-dominance property of the secondary path in the wave domain. The proposed wave-domain secondary path model is applicable to both concentric and nonconcentric circular loudspeakers and microphone array placement, and is also robust against array positioning errors. Normalized least mean squares-type algorithms are adopted for adaptive feedback control. Computational complexity is analyzed and compared with the conventional time-domain and frequency-domain multichannel ANCs. Through simulation-based verification in comparison with existing methods, the proposed algorithm demonstrates more efficient adaptation with low-level auxiliary noise.DP14010341

Noise cancellation over spatial regions using adaptive wave domain processing

This paper proposes wave-domain adaptive processing for noise cancellation within a large spatial region. We use fundamental solutions of the Helmholtz wave-equation as basis functions to express the noise field over a spatial region and show the wave-domain processing directly on the decomposition coefficients to control the entire region. A feedback control system is implemented, where only a single microphone array is placed at the boundary of the control region to measure the residual signals, and a loudspeaker array is used to generate the anti-noise signals. We develop the adaptive wave-domain filtered-x least mean square algorithm. Simulation results show that using the proposed method the noise over the entire control region can be significantly reduced with fast convergence in both free-field and reverberant environmentsThanks to Australian Research Councils Discovery Projects funding scheme (project no. DP140103412). The work of J. Zhang was sponsored by the China Scholarship Council with the Australian National University

An Innovations-Based Noise Cancelling Technique on Inverse Kepstrum Whitening Filter and Adaptive FIR Filter in Beamforming Structure

This paper presents an acoustic noise cancelling technique using an inverse kepstrum system as an innovations-based whitening application for an adaptive finite impulse response (FIR) filter in beamforming structure. The inverse kepstrum method uses an innovations-whitened form from one acoustic path transfer function between a reference microphone sensor and a noise source so that the rear-end reference signal will then be a whitened sequence to a cascaded adaptive FIR filter in the beamforming structure. By using an inverse kepstrum filter as a whitening filter with the use of a delay filter, the cascaded adaptive FIR filter estimates only the numerator of the polynomial part from the ratio of overall combined transfer functions. The test results have shown that the adaptive FIR filter is more effective in beamforming structure than an adaptive noise cancelling (ANC) structure in terms of signal distortion in the desired signal and noise reduction in noise with nonminimum phase components. In addition, the inverse kepstrum method shows almost the same convergence level in estimate of noise statistics with the use of a smaller amount of adaptive FIR filter weights than the kepstrum method, hence it could provide better computational simplicity in processing. Furthermore, the rear-end inverse kepstrum method in beamforming structure has shown less signal distortion in the desired signal than the front-end kepstrum method and the front-end inverse kepstrum method in beamforming structure

Robust Automatic Speech recognition System Implemented in a Hybrid Design DSP-FPGA

The aim of this work is to reduce the burden task on the DSP processor by transferring a parallel computation part on a configurable circuits FPGA, in automatic speech recognition module design, signal pre-processing, feature selection and optimization, models construction and finally classification phase are necessary. LMS filter algorithm that contains more parallelism and more MACs (multiply and Accumulate) operations is implemented on FPGA Virtex 5 by Xilings, MFCCs features extraction and DTW ( dynamic time wrapping) method is used as a classifier. Major contribution of this work are hybrid solution DSP and FPGA in real time speech recognition system design, the optimization of number of MAC-core within the FPGA this result is obtained by sharing MAC resources between two operation phases: computation of output filter and updating LMS filter coefficients. The paper also provides a hardware solution of the filter with detailed description of asynchronous interface of FPGA circuit and TMS320C6713-EMIF component. The results of simulation shows an improvement in time computation and by optimizing the implementation on the FPGA a gain in space consumption is obtained

Adaptive Algorithms for Intelligent Acoustic Interfaces

Modern speech communications are evolving towards a new direction which involves users in a more perceptive way. That is the immersive experience, which may be considered as the “last-mile” problem of telecommunications. One of the main feature of immersive communications is the distant-talking, i.e. the hands-free (in the broad sense) speech communications without bodyworn or tethered microphones that takes place in a multisource environment where interfering signals may degrade the communication quality and the intelligibility of the desired speech source. In order to preserve speech quality intelligent acoustic interfaces may be used. An intelligent acoustic interface may comprise multiple microphones and loudspeakers and its peculiarity is to model the acoustic channel in order to adapt to user requirements and to environment conditions. This is the reason why intelligent acoustic interfaces are based on adaptive filtering algorithms. The acoustic path modelling entails a set of problems which have to be taken into account in designing an adaptive filtering algorithm. Such problems may be basically generated by a linear or a nonlinear process and can be tackled respectively by linear or nonlinear adaptive algorithms. In this work we consider such modelling problems and we propose novel effective adaptive algorithms that allow acoustic interfaces to be robust against any interfering signals, thus preserving the perceived quality of desired speech signals. As regards linear adaptive algorithms, a class of adaptive filters based on the sparse nature of the acoustic impulse response has been recently proposed. We adopt such class of adaptive filters, named proportionate adaptive filters, and derive a general framework from which it is possible to derive any linear adaptive algorithm. Using such framework we also propose some efficient proportionate adaptive algorithms, expressly designed to tackle problems of a linear nature. On the other side, in order to address problems deriving from a nonlinear process, we propose a novel filtering model which performs a nonlinear transformations by means of functional links. Using such nonlinear model, we propose functional link adaptive filters which provide an efficient solution to the modelling of a nonlinear acoustic channel. Finally, we introduce robust filtering architectures based on adaptive combinations of filters that allow acoustic interfaces to more effectively adapt to environment conditions, thus providing a powerful mean to immersive speech communications

Adaptive Algorithms for Intelligent Acoustic Interfaces

Modern speech communications are evolving towards a new direction which involves users in a more perceptive way. That is the immersive experience, which may be considered as the “last-mile” problem of telecommunications. One of the main feature of immersive communications is the distant-talking, i.e. the hands-free (in the broad sense) speech communications without bodyworn or tethered microphones that takes place in a multisource environment where interfering signals may degrade the communication quality and the intelligibility of the desired speech source. In order to preserve speech quality intelligent acoustic interfaces may be used. An intelligent acoustic interface may comprise multiple microphones and loudspeakers and its peculiarity is to model the acoustic channel in order to adapt to user requirements and to environment conditions. This is the reason why intelligent acoustic interfaces are based on adaptive filtering algorithms. The acoustic path modelling entails a set of problems which have to be taken into account in designing an adaptive filtering algorithm. Such problems may be basically generated by a linear or a nonlinear process and can be tackled respectively by linear or nonlinear adaptive algorithms. In this work we consider such modelling problems and we propose novel effective adaptive algorithms that allow acoustic interfaces to be robust against any interfering signals, thus preserving the perceived quality of desired speech signals. As regards linear adaptive algorithms, a class of adaptive filters based on the sparse nature of the acoustic impulse response has been recently proposed. We adopt such class of adaptive filters, named proportionate adaptive filters, and derive a general framework from which it is possible to derive any linear adaptive algorithm. Using such framework we also propose some efficient proportionate adaptive algorithms, expressly designed to tackle problems of a linear nature. On the other side, in order to address problems deriving from a nonlinear process, we propose a novel filtering model which performs a nonlinear transformations by means of functional links. Using such nonlinear model, we propose functional link adaptive filters which provide an efficient solution to the modelling of a nonlinear acoustic channel. Finally, we introduce robust filtering architectures based on adaptive combinations of filters that allow acoustic interfaces to more effectively adapt to environment conditions, thus providing a powerful mean to immersive speech communications

On the Suppression of Noise from a Fast Moving Acoustic Source using Multimodality

International audienceThe problem of cancelling the noise from a moving acoustic source in outdoor environment is investigated in this paper. By making use of the known instantaneous location of the moving source (provided by a second modality), we propose a time-domain method for removing the noise from a moving source in a mixture of acoustic sources. The proposed method consists in resampling the mixed data recorded at a reference sensor, and by linearly combining the resampled data and the non-resampled data of the others sensor to cancel the undesired source. Simulation on synthetic data show the effectiveness and the usefulness of the proposed method

Speech processing using digital MEMS microphones

The last few years have seen the start of a unique change in microphones for consumer devices such as smartphones or tablets. Almost all analogue capacitive microphones are being replaced by digital silicon microphones or MEMS microphones. MEMS microphones perform differently to conventional analogue microphones. Their greatest disadvantage is significantly increased self-noise or decreased SNR, while their most significant benefits are ease of design and manufacturing and improved sensitivity matching. This thesis presents research on speech processing, comparing conventional analogue microphones with the newly available digital MEMS microphones. Specifically, voice activity detection, speaker diarisation (who spoke when), speech separation and speech recognition are looked at in detail. In order to carry out this research different microphone arrays were built using digital MEMS microphones and corpora were recorded to test existing algorithms and devise new ones. Some corpora that were created for the purpose of this research will be released to the public in 2013. It was found that the most commonly used VAD algorithm in current state-of-theart diarisation systems is not the best-performing one, i.e. MLP-based voice activity detection consistently outperforms the more frequently used GMM-HMM-based VAD schemes. In addition, an algorithm was derived that can determine the number of active speakers in a meeting recording given audio data from a microphone array of known geometry, leading to improved diarisation results. Finally, speech separation experiments were carried out using different post-filtering algorithms, matching or exceeding current state-of-the art results. The performance of the algorithms and methods presented in this thesis was verified by comparing their output using speech recognition tools and simple MLLR adaptation and the results are presented as word error rates, an easily comprehensible scale. To summarise, using speech recognition and speech separation experiments, this thesis demonstrates that the significantly reduced SNR of the MEMS microphone can be compensated for with well established adaptation techniques such as MLLR. MEMS microphones do not affect voice activity detection and speaker diarisation performance