Analysis of a non-minimum phase acoustic beamformer

The two input Griffiths-Jim acoustic beamformer is analysed in the frequency domain using a Wiener type formulation. Unlike previous solutions the approach here is to look at the problem of non-minimum phase acoustic transfer functions which are encountered in many real filtering problems. The polynomial transfer function approach gives an elegant way of obtaining the frequency response of the beamformer and gives new insight to the problem in general

A robust sequential hypothesis testing method for brake squeal localisation

This contribution deals with the in situ detection and localisation of brake squeal in an automobile. As brake squeal is emitted from regions known a priori, i.e., near the wheels, the localisation is treated as a hypothesis testing problem. Distributed microphone arrays, situated under the automobile, are used to capture the directional properties of the sound field generated by a squealing brake. The spatial characteristics of the sampled sound field is then used to formulate the hypothesis tests. However, in contrast to standard hypothesis testing approaches of this kind, the propagation environment is complex and time-varying. Coupled with inaccuracies in the knowledge of the sensor and source positions as well as sensor gain mismatches, modelling the sound field is difficult and standard approaches fail in this case. A previously proposed approach implicitly tried to account for such incomplete system knowledge and was based on ad hoc likelihood formulations. The current paper builds upon this approach and proposes a second approach, based on more solid theoretical foundations, that can systematically account for the model uncertainties. Results from tests in a real setting show that the proposed approach is more consistent than the prior state-of-the-art. In both approaches, the tasks of detection and localisation are decoupled for complexity reasons. The localisation (hypothesis testing) is subject to a prior detection of brake squeal and identification of the squeal frequencies. The approaches used for the detection and identification of squeal frequencies are also presented. The paper, further, briefly addresses some practical issues related to array design and placement. (C) 2019 Author(s)

Time-Frequency Masking Performance for Improved Intelligibility with Microphone Arrays

Time-Frequency (TF) masking is an audio processing technique useful for isolating an audio source from interfering sources. TF masking has been applied and studied in monaural and binaural applications, but has only recently been applied to distributed microphone arrays. This work focuses on evaluating the TF masking technique\u27s ability to isolate human speech and improve speech intelligibility in an immersive cocktail party environment. In particular, an upper-bound on TF masking performance is established and compared to the traditional delay-sum and general sidelobe canceler (GSC) beamformers. Additionally, the novel technique of combining the GSC with TF masking is investigated and its performance evaluated. This work presents a resource-efficient method for studying the performance of these isolation techniques and evaluates their performance using both virtually simulated data and data recorded in a real-life acoustical environment. Further, methods are presented to analyze speech intelligibility post-processing, and automated objective intelligibility measurements are applied alongside informal subjective assessments to evaluate the performance of these processing techniques. Finally, the causes for subjective/objective intelligibility measurement disagreements are discussed, and it was shown that TF masking did enhance intelligibility beyond delay-sum beamforming and that the utilization of adaptive beamforming can be beneficial

Spatial, Spectral, and Perceptual Nonlinear Noise Reduction for Hands-free Microphones in a Car

Speech enhancement in an automobile is a challenging problem because interference can come from engine noise, fans, music, wind, road noise, reverberation, echo, and passengers engaging in other conversations. Hands-free microphones make the situation worse because the strength of the desired speech signal reduces with increased distance between the microphone and talker. Automobile safety is improved when the driver can use a hands-free interface to phones and other devices instead of taking his eyes off the road. The demand for high quality hands-free communication in the automobile requires the introduction of more powerful algorithms. This thesis shows that a unique combination of five algorithms can achieve superior speech enhancement for a hands-free system when compared to beamforming or spectral subtraction alone. Several different designs were analyzed and tested before converging on the configuration that achieved the best results. Beamforming, voice activity detection, spectral subtraction, perceptual nonlinear weighting, and talker isolation via pitch tracking all work together in a complementary iterative manner to create a speech enhancement system capable of significantly enhancing real world speech signals. The following conclusions are supported by the simulation results using data recorded in a car and are in strong agreement with theory. Adaptive beamforming, like the Generalized Side-lobe Canceller (GSC), can be effectively used if the filters only adapt during silent data frames because too much of the desired speech is cancelled otherwise. Spectral subtraction removes stationary noise while perceptual weighting prevents the introduction of offensive audible noise artifacts. Talker isolation via pitch tracking can perform better when used after beamforming and spectral subtraction because of the higher accuracy obtained after initial noise removal. Iterating the algorithm once increases the accuracy of the Voice Activity Detection (VAD), which improves the overall performance of the algorithm. Placing the microphone(s) on the ceiling above the head and slightly forward of the desired talker appears to be the best location in an automobile based on the experiments performed in this thesis. Objective speech quality measures show that the algorithm removes a majority of the stationary noise in a hands-free environment of an automobile with relatively minimal speech distortion

Improving speech intelligibility in hearing aids. Part I: Signal processing algorithms

[EN] The improvement of speech intelligibility in hearing aids is a traditional problem that still remains open and unsolved. Modern devices may include signal processing algorithms to improve intelligibility: automatic gain control, automatic environmental classification or speech enhancement. However, the design of such algorithms is strongly restricted by some engineering constraints caused by the reduced dimensions of hearing aid devices. In this paper, we discuss the application of state-of-theart signal processing algorithms to improve speech intelligibility in digital hearing aids, with particular emphasis on speech enhancement algorithms. Different alternatives for both monaural and binaural speech enhancement have been considered, arguing whether they are suitable to be implemented in a commercial hearing aid or not.This work has been funded by the Spanish Ministry of Science and Innovation, under project TEC2012-38142-C04-02.Ayllón, D.; Gil Pita, R.; Rosa Zurera, M.; Padilla, L.; Piñero Sipán, MG.; Diego Antón, MD.; Ferrer Contreras, M.... (2014). Improving speech intelligibility in hearing aids. Part I: Signal processing algorithms. Waves. 6:61-71. http://hdl.handle.net/10251/57901S6171

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

Blind Beamforming on a Randomly Distributed Sensor Array System

We consider a digital signal handling sensor array system, in light of haphazardly dispersed sensor node, for observation and source localization applications. In most array handling system, the sensor array geometry is settled and known and the steering array vector/complex data is utilized as a part of beam- formation. In this system, the array adjustment may be illogical because of obscure situation and introduction of the sensors with obscure frequency/spatial responses.In this project work a blind beamforming method is used by utilizing just the deliberate sensor information, to shape either an example information or a sample correlation matrix. The greatest power accumulation measure is utilized to acquire array weights from the predominant eigenvector connected with the largest eigenvalue of a matrix eigenvalue issue. A productive blind beamforming time delay appraisal of the predominant source is proposed. Source localization in light of a least squares (LS) technique for time delay estimation is additionally given. Results taking into account investigation, simulation, and measured acoustical sensor information demonstrate the viability of this beamforming system for sign upgrade and spacetime filtering

A Survey on Application Specific Processor Architectures for Digital Hearing Aids

On the one hand, processors for hearing aids are highly specialized for audio processing, on the other hand they have to meet challenging hardware restrictions. This paper aims to provide an overview of the requirements, architectures, and implementations of these processors. Special attention is given to the increasingly common application-specific instruction-set processors (ASIPs). The main focus of this paper lies on hardware-related aspects such as the processor architecture, the interfaces, the application specific integrated circuit (ASIC) technology, and the operating conditions. The different hearing aid implementations are compared in terms of power consumption, silicon area, and computing performance for the algorithms used. Challenges for the design of future hearing aid processors are discussed based on current trends and developments