Imperial College London

DrAlastairMoore

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Research Fellow in Acoustic Signal Processing
 
 
 
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Contact

 

alastair.h.moore

 
 
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Location

 

809Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

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60 results found

Moore A, Vos R, Naylor P, Brookes Det al., 2021, Processing pipelines for efficient, physically-accurate simulation of microphone array signals in dynamic sound scenes, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, Publisher: IEEE, ISSN: 0736-7791

Multichannel acoustic signal processing is predicated on the fact that the inter channel relationships between the received signals can be exploited to infer information about the acoustic scene. Recently there has been increasing interest in algorithms which are applicable in dynamic scenes, where the source(s) and/or microphone array may be moving. Simulating such scenes has particular challenges which are exacerbated when real-time, listener-in-the-loop evaluation of algorithms is required. This paper considers candidate pipelines for simulating the array response to a set of point/image sources in terms of their accuracy, scalability and continuity. Anew approach, in which the filter kernels are obtained using principal component analysis from time-aligned impulse responses, is proposed. When the number of filter kernels is≤40the new approach achieves more accurate simulation than competing methods.

Conference paper

D'Olne E, Moore A, Naylor P, 2021, Model-based beamforming for wearable microphone arrays, European Signal Processing Conference (EUSIPCO), Publisher: IEEE

Beamforming techniques for hearing aid applications are often evaluated using behind-the-ear (BTE) devices. However, the growing number of wearable devices with microphones has made it possible to consider new geometries for microphone array beamforming. In this paper, we examine the effect of array location and geometry on the performance of binaural minimum power distortionless response (BMPDR) beamformers. In addition to the classical adaptive BMPDR, we evaluate the benefit of a recently-proposed method that estimates the sample covariance matrix using a compact model. Simulation results show that using a chest-mounted array reduces noise by an additional 1.3~dB compared to BTE hearing aids. The compact model method is found to yield higher predicted intelligibility than adaptive BMPDR beamforming, regardless of the array geometry.

Conference paper

Hogg A, Evers C, Moore A, Naylor Pet al., 2021, Overlapping speaker segmentation using multiple hypothesis tracking of fundamental frequency, IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol: 29, Pages: 1479-1490, ISSN: 2329-9290

This paper demonstrates how the harmonic structure of voiced speech can be exploited to segment multiple overlapping speakers in a speaker diarization task. We explore how a change in the speaker can be inferred from a change in pitch. We show that voiced harmonics can be useful in detecting when more than one speaker is talking, such as during overlapping speaker activity. A novel system is proposed to track multiple harmonics simultaneously, allowing for the determination of onsets and end-points of a speaker’s utterance in the presence of an additional active speaker. This system is bench-marked against a segmentation system from the literature that employs a bidirectional long short term memory network (BLSTM) approach and requires training. Experimental results highlight that the proposed approach outperforms the BLSTM baseline approach by 12.9% in terms of HIT rate for speaker segmentation. We also show that the estimated pitch tracks of our system can be used as features to the BLSTM to achieve further improvements of 1.21% in terms of coverage and 2.45% in terms of purity

Journal article

Hafezi S, Moore A, Naylor P, 2021, Narrowband multi-source Direction-of-Arrival estimation in the spherical harmonic domain, Journal of the Acoustical Society of America, Vol: 149, ISSN: 0001-4966

A conventional approach to wideband multi-source (MS) direction-of-arrival (DOA) estimation is to perform single source (SS) DOA estimation in time-frequency (TF) bins for which a SS assumption is valid. Such methods use the W-disjoint orthogonality (WDO) assumption due to the speech sparseness. As the number of sources increases, the chance of violating the WDO assumption increases. As shown in the challenging scenarios with multiple simultaneously active sources over a short period of time masking each other, it is possible for a strongly masked source (due to inconsistency of activity or quietness) to be rarely dominant in a TF bin. SS-based DOA estimators fail in the detection or accurate localization of masked sources in such scenarios. Two analytical approaches are proposed for narrowband DOA estimation based on the MS assumption in a bin in the spherical harmonic domain. In the first approach, eigenvalue decomposition is used to decompose a MS scenario into multiple SS scenarios, and a SS-based analytical DOA estimation is performed on each. The second approach analytically estimates two DOAs per bin assuming the presence of two active sources per bin. The evaluation validates the improvement to double accuracy and robustness to sensor noise compared to the baseline methods.

Journal article

Xue W, Moore A, Brookes D, Naylor Pet al., 2020, Speech enhancement based on modulation-domain parametric multichannel Kalman filtering, IEEE Transactions on Audio, Speech and Language Processing, Vol: 29, Pages: 393-405, ISSN: 1558-7916

Recently we presented a modulation-domain multichannel Kalman filtering (MKF) algorithm for speech enhancement, which jointly exploits the inter-frame modulation-domain temporal evolution of speech and the inter-channel spatial correlation to estimate the clean speech signal. The goal of speech enhancement is to suppress noise while keeping the speech undistorted, and a key problem is to achieve the best trade-off between speech distortion and noise reduction. In this paper, we extend the MKF by presenting a modulation-domain parametric MKF (PMKF) which includes a parameter that enables flexible control of the speech enhancement behaviour in each time-frequency (TF) bin. Based on the decomposition of the MKF cost function, a new cost function for PMKF is proposed, which uses the controlling parameter to weight the noise reduction and speech distortion terms. An optimal PMKF gain is derived using a minimum mean squared error (MMSE) criterion. We analyse the performance of the proposed MKF, and show its relationship to the speech distortion weighted multichannel Wiener filter (SDW-MWF). To evaluate the impact of the controlling parameter on speech enhancement performance, we further propose PMKF speech enhancement systems in which the controlling parameter is adaptively chosen in each TF bin. Experiments on a publicly available head-related impulse response (HRIR) database in different noisy and reverberant conditions demonstrate the effectiveness of the proposed method.

Journal article

Hafezi S, Moore AH, Naylor PA, 2019, Spatial consistency for multiple source direction-of-arrival estimation and source counting., Journal of the Acoustical Society of America, Vol: 146, Pages: 4592-4603, ISSN: 0001-4966

A conventional approach to wideband multi-source (MS) direction-of-arrival (DOA) estimation is to perform single source (SS) DOA estimation in time-frequency (TF) bins for which a SS assumption is valid. The typical SS-validity confidence metrics analyse the validity of the SS assumption over a fixed-size TF region local to the TF bin. The performance of such methods degrades as the number of simultaneously active sources increases due to the associated decrease in the size of the TF regions where the SS assumption is valid. A SS-validity confidence metric is proposed that exploits a dynamic MS assumption over relatively larger TF regions. The proposed metric first clusters the initial DOA estimates (one per TF bin) and then uses the members' spatial consistency as well as its cluster's spread to weight each TF bin. Distance-based and density-based clustering are employed as two alternative approaches for clustering DOAs. A noise-robust density-based clustering is also used in an evolutionary framework to propose a method for source counting and source direction estimation. The evaluation results based on simulations and also with real recordings show that the proposed weighting strategy significantly improves the accuracy of source counting and MS DOA estimation compared to the state-of-the-art.

Journal article

Moore AH, de Haan JM, Pedersen MS, Brookes D, Naylor PA, Jensen Jet al., 2019, Personalized signal-independent beamforming for binaural hearing aids, 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.

Journal article

Moore A, Xue W, Naylor P, Brookes Det al., 2019, Noise covariance matrix estimation for rotating microphone arrays, 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.

Journal article

Moore A, de Haan JM, Pedersen MS, Naylor P, Brookes D, Jensen Jet al., 2019, Personalized {HRTF}s for hearing aids, ELOBES2019

Conference paper

Brookes D, Lightburn L, Moore A, Naylor P, Xue Wet al., 2019, Mask-assisted speech enhancement for binaural hearing aids, ELOBES2019

Conference paper

Xue W, Moore AH, Brookes M, Naylor PAet al., 2018, Modulation-domain parametric multichannel kalman filtering for speech enhancement, European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 2509-2513, ISSN: 2076-1465

The 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.

Conference paper

Moore AJS, Dean LSN, Fraceto LF, Lima R, Tetley TDet al., 2018, THE EFFECTS OF A NOVEL POLY(EPSILON-CAPROLACTONE) NANOCAPSULE CONTAINING THE PESTICIDE ATRAZINE ON HUMAN ALVEOLAR EPITHELIUM, Winter Meeting of the British-Thoracic-Society, Publisher: BMJ PUBLISHING GROUP, Pages: A20-A21, ISSN: 0040-6376

Conference paper

Moore AH, 2018, Multiple source direction of arrival estimation using subspace pseudointensity vectors, Publisher: arXiv

The recently proposed subspace pseudointensity method for direction ofarrival estimation is applied in the context of Tasks 1 and 2 of the LOCATAChallenge using the Eigenmike recordings. Specific implementation details aredescribed and results reported for the development dataset, for which theground truth source directions are available. For both single and multiplesource scenarios, the average absolute error angle is about 9 degrees.

Working paper

Moore AH, Lightburn L, Xue W, Naylor P, Brookes Det al., 2018, Binaural mask-informed speech enhancement for hearing aids with head tracking, 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.

Conference paper

Moore AH, Xue W, Naylor PA, Brookes Met 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

Conference paper

Xue W, Moore A, Brookes DM, Naylor Pet al., 2018, Modulation-domain multichannel Kalman filtering for speech enhancement, 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.

Journal article

Yiallourides C, Moore AH, Auvinet E, Van der Straeten C, Naylor PAet al., 2018, Acoustic Analysis and Assessment of the Knee in Osteoarthritis During Walking, 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%.

Conference paper

Xue W, Moore A, Brookes DM, Naylor Pet al., 2018, Multichannel kalman filtering for speech ehnancement, 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.

Conference paper

Moore AH, Naylor P, Brookes DM, 2018, Room identification using frequency dependence of spectral decay statistics, 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.

Conference paper

Hafezi 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

Conference paper

D'Amore L, Arcucci R, Li Y, Montella R, Moore A, Phillipson L, Toumi Ret al., 2018, Performance Assessment of the Incremental Strong Constraints 4DVAR Algorithm in ROMS, 12th International Conference on Parallel Processing and Applied Mathematics (PPAM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 48-57, ISSN: 0302-9743

Conference paper

Moore AH, Peso Parada P, Naylor PA, 2017, Speech enhancement for robust automatic speech recognition: Evaluation using a baseline system and instrumental measures, Computer Speech & Language, Vol: 46, Pages: 574-584, ISSN: 0885-2308

Journal article

Hafezi S, Moore AH, Naylor PA, 2017, Multiple DOA estimation based on estimation consistency and spherical harmonic multiple signal classification, 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.

Conference paper

Hafezi S, Moore AH, Naylor PATRICK, 2017, Augmented Intensity Vectors for Direction of Arrival Estimation in the Spherical Harmonic Domain, 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.

Journal article

Eaton DJ, Gaubitch ND, Moore AH, Naylor PAet al., 2017, Acoustic Characterization of Environments (ACE) Challenge Results Technical Report, Publisher: arXiv

This 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.

Report

Hafezi S, Moore AH, Naylor P, 2017, Multiple source localization using estimation consistency in the time-frequency domain, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: Institute of Electrical and Electronics Engineers (IEEE), Pages: 516-520, ISSN: 1520-6149

The extraction of multiple Direction-of-Arrival (DoA) information from estimated spatial spectra can be challenging when such spectra are noisy or the sources are adjacent. Smoothing or clustering techniques are typically used to remove the effect of noise or irregular peaks in the spatial spectra. As we will explain and show in this paper, the smoothing-based techniques require prior knowledge of minimum angular separation of the sources and the clustering-based techniques fail on noisy spatial spectrum. A broad class of localization techniques give direction estimates in each Time Frequency (TF) bin. Using this information as input, a novel technique for obtaining robust localization of multiple simultaneous sources is proposed using Estimation Consistency (EC) in the TF domain. The method is evaluated in the context of spherical microphone arrays. This technique does not require prior knowledge of the sources and by removing the noise in the estimated spatial spectrum makes clustering a reliable and robust technique for multiple DoA extraction from estimated spatial spectra. The results indicate that the proposed technique has the strongest robustness to separation with up to 10° median error for 5° to 180° separation for 2 and 3 sources, compared to the baseline and the state-of-the-art techniques.

Conference paper

Yiallourides C, Manning V, Moore AH, Naylor Pet al., 2017, A dynamic programming approach for automatic stride detection and segmentation in acoustic emission from the knee, 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.

Conference paper

Lightburn L, De Sena E, Moore AH, Naylor PA, Brookes Det al., 2017, Improving the perceptual quality of ideal binary masked speech, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: Institute of Electrical and Electronics Engineers (IEEE), Pages: 661-665, ISSN: 1520-6149

It is known that applying a time-frequency binary mask to very noisy speech can improve its intelligibility but results in poor perceptual quality. In this paper we propose a new approach to applying a binary mask that combines the intelligibility gains of conventional binary masking with the perceptual quality gains of a classical speech enhancer. The binary mask is not applied directly as a time-frequency gain as in most previous studies. Instead, the mask is used to supply prior information to a classical speech enhancer about the probability of speech presence in different time-frequency regions. Using an oracle ideal binary mask, we show that the proposed method results in a higher predicted quality than other methods of applying a binary mask whilst preserving the improvements in predicted intelligibility.

Conference paper

Moore AH, Brookes D, Naylor PA, 2017, Robust spherical harmonic domain interpolation of spatially sampled array manifolds, IEEE International Conference on Acoustics Speech and Signal Processing, Publisher: Institute of Electrical and Electronics Engineers (IEEE), Pages: 521-525, ISSN: 1520-6149

Accurate interpolation of the array manifold is an important firststep for the acoustic simulation of rapidly moving microphone ar-rays. Spherical harmonic domain interpolation has been proposedand well studied in the context of head-related transfer functions buthas focussed on perceptual, rather than numerical, accuracy. In thispaper we analyze the effect of measurement noise on spatial aliasing.Based on this analysis we propose a method for selecting the trunca-tion orders for the forward and reverse spherical Fourier transformsgiven only the noisy samples in such a way that the interpolationerror is minimized. The proposed method achieves up to 1.7 dB im-provement over the baseline approach.

Conference paper

Hafezi S, Moore AH, Naylor PA, 2017, Multi-source estimation consistency for improved multiple direction-of-arrival estimation, Joint Workshop on Hands-free Speech Communication and Microphone Arrays, Publisher: IEEE, Pages: 81-85

In Direction-of-Arrival (DOA) estimation for multiple sources, removal of noisy data points from a set of local DOA estimates increases the resulting estimation accuracy, especially when there are many sources and they have small angular separation. In this work, we propose a post-processing technique for the enhancement of DOA extraction from a set of local estimates using the consistency of these estimates within the time frame based on adaptive multi-source assumption. Simulations in a realistic reverberant environment with sensor noise and up to 5 sources demonstrate that the proposed technique outperforms the baseline and state-of-the-art approaches. In these tests the proposed technique had the worst average error of 9°, robustness of 5° to widely varying source separation and 3° to number of sources.

Conference paper

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