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Conference paperSharma D, Hogg AOT, Wang Y, et al., 2019, , 2019 27th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 1-5
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Journal articleMoore AH, de Haan JM, Pedersen MS, et al., 2019, , Journal of the Acoustical Society of America, Vol: 145, Pages: 2971-2981, ISSN: 0001-4966
The effect of personalized microphone array calibration on the performance of hearing aid beamformers under noisy reverberant conditions is studied. The study makes use of a new, publicly available, database containing acoustic transfer function measurements from 29 loudspeakers arranged on a sphere to a pair of behind-the-ear hearing aids in a listening room when worn by 27 males, 14 females, and 4 mannequins. Bilateral and binaural beamformers are designed using each participant's hearing aid head-related impulse responses (HAHRIRs). The performance of these personalized beamformers is compared to that of mismatched beamformers, where the HAHRIR used for the design does not belong to the individual for whom performance is measured. The case where the mismatched HAHRIR is that of a mannequin is of particular interest since it represents current practice in commercially available hearing aids. The benefit of personalized beamforming is assessed using an intrusive binaural speech intelligibility metric and in a matrix speech intelligibility test. For binaural beamforming, both measures demonstrate a statistically signficant (p < 0.05) benefit of personalization. The benefit varies substantially between individuals with some predicted to benefit by as much as 1.5 dB.
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Conference paperHogg AOT, Evers C, Naylor PA, 2019, , ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 5826-5830
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Journal articleMoore A, Xue W, Naylor P, et al., 2019, , IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol: 27, Pages: 519-530, ISSN: 2329-9290
The noise covariance matrix computed between the signals from a microphone array is used in the design of spatial filters and beamformers with applications in noise suppression and dereverberation. This paper specifically addresses the problem of estimating the covariance matrix associated with a noise field when the array is rotating during desired source activity, as is common in head-mounted arrays. We propose a parametric model that leads to an analytical expression for the microphone signal covariance as a function of the array orientation and array manifold. An algorithm for estimating the model parameters during noise-only segments is proposed and the performance shown to be improved, rather than degraded, by array rotation. The stored model parameters can then be used to update the covariance matrix to account for the effects of any array rotation that occurs when the desired source is active. The proposed method is evaluated in terms of the Frobenius norm of the error in the estimated covariance matrix and of the noise reduction performance of a minimum variance distortionless response beamformer. In simulation experiments the proposed method achieves 18 dB lower error in the estimated noise covariance matrix than a conventional recursive averaging approach and results in noise reduction which is within 0.05 dB of an oracle beamformer using the ground truth noise covariance matrix.
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Conference paperBrookes D, Lightburn L, Moore A, et al., 2019,
Mask-assisted speech enhancement for binaural hearing aids
, ELOBES2019 -
Conference paperMoore A, de Haan JM, Pedersen MS, et al., 2019,
Personalized {HRTF}s for hearing aids
, ELOBES2019 -
Conference paperXue W, Moore AH, Brookes M, et al., 2018,
Modulation-domain parametric multichannel kalman filtering for speech enhancement
, European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 2509-2513, ISSN: 2076-1465The goal of speech enhancement is to reduce the noise signal while keeping the speech signal undistorted. Recently we developed the multichannel Kalman filtering (MKF) for speech enhancement, in which the temporal evolution of the speech signal and the spatial correlation between multichannel observations are jointly exploited to estimate the clean signal. In this paper, we extend the previous work to derive a parametric MKF (PMKF), which incorporates a controlling factor to achieve the trade-off between the speech distortion and noise reduction. The controlling factor weights between the speech distortion and noise reduction related terms in the cost function of PMKF, and based on the minimum mean squared error (MMSE) criterion, the optimal PMKF gain is derived. We analyse the performance of the proposed PMKF and show the differences with the speech distortion weighted multichannel Wiener filter (SDW-MWF). We conduct experiments in different noisy conditions to evaluate the impact of the controlling factor on the noise reduction performance, and the results demonstrate the effectiveness of the proposed method.
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Conference paperMoore AH, Lightburn L, Xue W, et al., 2018, , International Workshop on Acoustic Signal Enhancement (IWAENC 2018), Publisher: IEEE, Pages: 461-465
An end-to-end speech enhancement system for hearing aids is pro-posed which seeks to improve the intelligibility of binaural speechin noise during head movement. The system uses a reference beam-former whose look direction is informed by knowledge of the headorientation and the a priori known direction of the desired source.From this a time-frequency mask is estimated using a deep neuralnetwork. The binaural signals are obtained using bilateral beam-formers followed by a classical minimum mean square error speechenhancer, modified to use the estimated mask as a speech presenceprobability prior. In simulated experiments, the improvement in abinaural intelligibility metric (DBSTOI) given by the proposed sys-tem relative to beamforming alone corresponds to an SNR improve-ment of 4 to 6 dB. Results also demonstrate the individual contribu-tions of incorporating the mask and the head orientation-aware beamsteering to the proposed system.
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Conference paperMoore AH, Xue W, Naylor PA, et al., 2018,
Estimation of the Noise Covariance Matrix for Rotating Sensor Arrays
, 52nd Asilomar Conference on Signals, Systems, and Computers, Publisher: IEEE, Pages: 1936-1941, ISSN: 1058-6393 -
Journal articleXue W, Moore A, Brookes DM, et al., 2018, , IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol: 26, Pages: 1833-1847, ISSN: 2329-9290
Compared with single-channel speech enhancement methods, multichannel methods can utilize spatial information to design optimal filters. Although some filters adaptively consider second-order signal statistics, the temporal evolution of the speech spectrum is usually neglected. By using linear prediction (LP) to model the inter-frame temporal evolution of speech, single-channel Kalman filtering (KF) based methods have been developed for speech enhancement. In this paper, we derive a multichannel KF (MKF) that jointly uses both interchannel spatial correlation and interframe temporal correlation for speech enhancement. We perform LP in the modulation domain, and by incorporating the spatial information, derive an optimal MKF gain in the short-time Fourier transform domain. We show that the proposed MKF reduces to the conventional multichannel Wiener filter if the LP information is discarded. Furthermore, we show that, under an appropriate assumption, the MKF is equivalent to a concatenation of the minimum variance distortion response beamformer and a single-channel modulation-domain KF and therefore present an alternative implementation of the MKF. Experiments conducted on a public head-related impulse response database demonstrate the effectiveness of the proposed method.
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Conference paperXue W, Moore A, Brookes DM, et al., 2018, , IEEE Intl Conf on Acoustics, Speech and Signal Processing, Publisher: IEEE, ISSN: 2379-190X
The use of spatial information in multichannel speech enhancement methods is well established but information associated with the temporal evolution of speech is less commonly exploited. Speech signals can be modelled using an autoregressive process in the time-frequency modulation domain, and Kalman filtering based speech enhancement algorithms have been developed for single-channel processing. In this paper, a multichannel Kalman filter (MKF) for speech enhancement is derived that jointly considers the multichannel spatial information and the temporal correlations of speech. We model the temporal evolution of speech in the modulation domain and, by incorporating the spatial information, an optimal MKF gain is derived in the short-time Fourier transform domain. We also show that the proposed MKF becomes a conventional multichannel Wiener filter if the temporal information is discarded. Experiments using the signals generated from a public head-related impulse response database demonstrate the effectiveness of the proposed method in comparison to other techniques.
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Conference paperYiallourides C, Moore AH, Auvinet E, et al., 2018, , IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 281-285
We examine the relation between the sounds emitted by the knee joint during walking and its condition, with particular focus on osteoarthritis, and investigate their potential for noninvasive detection of knee pathology. We present a comparative analysis of several features and evaluate their discriminant power for the task of normal-abnormal signal classification. We statistically evaluate the feature distributions using the two-sample Kolmogorov-Smirnov test and the Bhattacharyya distance. We propose the use of 11 statistics to describe the distributions and test with several classifiers. In our experiments with 249 normal and 297 abnormal acoustic signals from 40 knees, a Support Vector Machine with linear kernel gave the best results with an error rate of 13.9%.
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Conference paperMoore AH, Naylor P, Brookes DM, 2018, , IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Publisher: Institute of Electrical and Electronics Engineers Inc., Pages: 6902-6906, ISSN: 0736-7791
A method for room identification is proposed based on the reverberation properties of multichannel speech recordings. The approach exploits the dependence of spectral decay statistics on the reverberation time of a room. The average negative-side variance within 1/3-octave bands is proposed as the identifying feature and shown to be effective in a classification experiment. However, negative-side variance is also dependent on the direct-to-reverberant energy ratio. The resulting sensitivity to different spatial configurations of source and microphones within a room are mitigated using a novel reverberation enhancement algorithm. A classification experiment using speech convolved with measured impulse responses and contaminated with environmental noise demonstrates the effectiveness of the proposed method, achieving 79% correct identification in the most demanding condition compared to 40% using unenhanced signals.
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Conference paperHafezi S, Moore AH, Naylor PA, 2018,
ROBUST SOURCE COUNTING AND ACOUSTIC DOA ESTIMATION USING DENSITY-BASED CLUSTERING
, 10th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Publisher: IEEE, Pages: 395-399, ISSN: 1551-2282 -
Journal articleDe Sena E, Brookes DM, Naylor PA, et al., 2017, , Journal of the Audio Engineering Society, Vol: 65, Pages: 982-996, ISSN: 0004-7554
This research focuses on sound localization experiments in which subjects report the position of an active sound source by turning toward it. A statistical framework for the analysis of the data is presented together with a case study from a large-scale listening experiment. The statistical framework is based on a model that is robust to the presence of front/back confusions and random errors. Closed-form natural estimators are derived, and one-sample and two-sample statistical tests are described. The framework is used to analyze the data of an auralized experiment undertaken by nearly nine hundred subjects. The objective was to explore localization performance in the horizontal plane in an informal setting and with little training, which are conditions that are similar to those typically encountered in consumer applications of binaural audio. Results show that responses had a rightward bias and that speech was harder to localize than percussion sounds, which are results consistent with the literature. Results also show that it was harder to localize sound in a simulated room with a high ceiling despite having a higher direct-to-reverberant ratio than other simulated rooms.
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Journal articleMoore AH, Peso Parada P, Naylor PA, 2017, , Computer Speech & Language, Vol: 46, Pages: 574-584, ISSN: 0885-2308
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Conference paperHafezi S, Moore AH, Naylor PA, 2017, , European Signal Processing Conference, EUSIPCO 2017, Pages: 1240-1244
© EURASIP 2017. A common approach to multiple Direction-of- Arrival (DOA) estimation of speech sources is to identify Time- Frequency (TF) bins with dominant Single Source (SS) and apply DOA estimation such as Multiple Signal Classification (MUSIC) only on those TF bins. In the state-of-the-art Direct Path Dominance (DPD)-MUSIC, the covariance matrix, used as the input to MUSIC, is calculated using only the TF bins over a local TF region where only a SS is dominant. In this work, we propose an alternative approach to MUSIC in which all the SS-dominant TF bins for each speaker across TF domain are globally used to improve the quality of covariance matrix for MUSIC. Our recently proposed Multi-Source Estimation Consistency (MSEC) technique, which exploits the consistency of initial DOA estimates within a time frame based on adaptive clustering, is used to estimate the SS-dominant TF bins for each speaker. The simulation using spherical microphone array shows that our proposed MSEC-MUSIC significantly outperforms the state-of-the-art DPD-MUSIC with less than 6:5° mean estimation error and strong robustness to widely varying source separation for up to 5 sources in the presence of realistic reverberation and sensor noise.
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Journal articleHafezi S, Moore AH, Naylor PATRICK, 2017, , IEEE Transactions on Audio, Speech and Language Processing, Vol: 25, Pages: 1956-1968, ISSN: 1558-7916
Pseudointensity vectors (PIVs) provide a means of direction of arrival (DOA) estimation for spherical microphone arrays using only the zeroth and the first-order spherical harmonics. An augmented intensity vector (AIV) is proposed which improves the accuracy of PIVs by exploiting higher order spherical harmonics. We compared DOA estimation using our proposed AIVs against PIVs, steered response power (SRP) and subspace methods where the number of sources, their angular separation, the reverberation time of the room and the sensor noise level are varied. The results show that the proposed approach outperforms the baseline methods and performs at least as accurately as the state-of-the-art method with strong robustness to reverberation, sensor noise, and number of sources. In the single and multiple source scenarios tested, which include realistic levels of reverberation and noise, the proposed method had average error of 1.5∘ and 2∘, respectively.
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ReportEaton DJ, Gaubitch ND, Moore AH, et al., 2017,
Acoustic Characterization of Environments (ACE) Challenge Results Technical Report
, Publisher: arXivThis document provides supplementary information, and the results of the tests of acoustic parameter estimation algorithms on the AcousticCharacterization of Environments (ACE) Challenge Evaluation dataset which were subsequently submitted and written up into papers for theProceedings of the ACE Challenge [2]. This document is supporting material for a forthcoming journal paper on the ACE Challenge which will provide further analysis of the results.
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Conference paperYiallourides C, Manning V, Moore AH, et al., 2017, , 2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), Publisher: Institute of Electrical and Electronics Engineers (IEEE), Pages: 401-405, ISSN: 1520-6149
We study the acquisition and analysis of sounds generated by the knee during walking with particular focus on the effects due to osteoarthritis. Reliable contact instant estimation is essential for stride synchronous analysis. We present a dynamic programming based algorithm for automatic estimation of both the initial contact instants (ICIs) and last contact instants (LCIs) of the foot to the floor. The technique is designed for acoustic signals sensed at the patella of the knee. It uses the phase-slope function to generate a set of candidates and then finds the most likely ones by minimizing a cost function that we define. ICIs are identified with an RMS error of 13.0% for healthy and 14.6% for osteoarthritic knees and LCIs with an RMS error of 16.0% and 17.0% respectively.
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