Imperial College London

DrAlastairMoore

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Research Associate
 
 
 
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Contact

 

alastair.h.moore

 
 
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Location

 

809Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

46 results found

Moore AH, Lightburn L, Xue W, Naylor PA, Brookes Met al., 2018, Binaural mask-informed speech enhancement for hearing AIDS with head tracking, Pages: 461-465

© 2018 IEEE. An end-to-end speech enhancement system for hearing aids is proposed which seeks to improve the intelligibility of binaural speech in noise during head movement. The system uses a reference beamformer whose look direction is informed by knowledge of the head orientation and the a priori known direction of the desired source. From this a time-frequency mask is estimated using a deep neural network. The binaural signals are obtained using bilateral beamformers followed by a classical minimum mean square error speech enhancer, modified to use the estimated mask as a speech presence probability prior. In simulated experiments, the improvement in a binaural intelligibility metric (DBSTOI) given by the proposed system relative to beamforming alone corresponds to an SNR improvement of 4 to 6 dB. Results also demonstrate the individual contributions of incorporating the mask and the head orientation-aware beam steering to the proposed system.

CONFERENCE PAPER

Xue W, Moore AH, Brookes M, Naylor PAet 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

JOURNAL ARTICLE

Hafezi S, Moore AH, Naylor PA, 2018, Robust source counting and acoustic DOA estimation using density-based clustering, Pages: 395-399

©2018 IEEE. Direction-of-Arrival (DOA) estimation for multiple simultaneously active acoustic sources without knowledge of the number of sources and the noise level remains a challenging task.A method of source counting for DOA estimation using density-based clustering is proposed. Multiple Density-based Spatial Clustering of Applications with Noise (DBSCAN) with varying noise sensitivity is applied in an evolutionary procedure to obtain weighted centroids.An autonomous DB-SCAN is finally run on the weighted centroids to extract the final DOA estimates. The results using generated and estimated DOAs show that the proposed technique significantly outperforms the conventional histogram peak picking as well as the original DBSCAN and variations of Kmeans with ≤4°DOA estimation accuracy and improves the source counting.

CONFERENCE PAPER

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

CONFERENCE PAPER

Moore AH, Naylor PA, Brookes M, 2018, ROOM IDENTIFICATION USING FREQUENCY DEPENDENCE OF SPECTRAL DECAY STATISTICS, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 6902-6906

CONFERENCE PAPER

Moore AH, Xue W, Naylor PA, Brookes Met al., 2018, Noise Covariance Matrix Estimation for Rotating Microphone Arrays, IEEE/ACM Transactions on Audio Speech and Language Processing, ISSN: 2329-9290

CCBY 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 which 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

Xue W, Moore AH, Brookes M, Naylor PAet al., 2018, MULTICHANNEL KALMAN FILTERING FOR SPEECH EHNANCEMENT, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 41-45

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 PA, 2017, Augmented Intensity Vectors for Direction of Arrival Estimation in the Spherical Harmonic Domain, IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, Vol: 25, Pages: 1956-1968, ISSN: 2329-9290

JOURNAL ARTICLE

Moore AH, Brookes M, Naylor PA, 2017, ROBUST SPHERICAL HARMONIC DOMAIN INTERPOLATION OF SPATIALLY SAMPLED ARRAY MANIFOLDS, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 521-525, ISSN: 1520-6149

CONFERENCE PAPER

Hafezi S, Moore AH, Naylor PA, 2017, MULTIPLE SOURCE LOCALIZATION USING ESTIMATION CONSISTENCY IN THE TIME-FREQUENCY DOMAIN, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 516-520, ISSN: 1520-6149

CONFERENCE PAPER

Hafezi S, Moore AH, Naylor PA, 2017, MULTI-SOURCE ESTIMATION CONSISTENCY FOR IMPROVED MULTIPLE DIRECTION-OF-ARRIVAL ESTIMATION, Conference on Hands-Free Speech Communications and Microphone Arrays (HSCMA), Publisher: IEEE, Pages: 81-85

CONFERENCE PAPER

Loellmann HW, Moore AH, Naylor PA, Rafaely B, Horaud R, Mazel A, Kellermann Wet al., 2017, MICROPHONE ARRAY SIGNAL PROCESSING FOR ROBOT AUDITION, Conference on Hands-Free Speech Communications and Microphone Arrays (HSCMA), Publisher: IEEE, Pages: 51-55

CONFERENCE PAPER

Yiallourides C, Manning-Eid V, Moore AH, Naylor PAet al., 2017, A DYNAMIC PROGRAMMING APPROACH FOR AUTOMATIC STRIDE DETECTION AND SEGMENTATION IN ACOUSTIC EMISSION FROM THE KNEE, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 401-405, ISSN: 1520-6149

CONFERENCE PAPER

Lightburn L, De Sena E, Moore A, Naylo PA, Brookes Met al., 2017, IMPROVING THE PERCEPTUAL QUALITY OF IDEAL BINARY MASKED SPEECH, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 661-665, ISSN: 1520-6149

CONFERENCE PAPER

Moore AH, Evers C, Naylor PA, 2017, Direction of Arrival Estimation in the Spherical Harmonic Domain Using Subspace Pseudointensity Vectors, IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, Vol: 25, Pages: 178-192, ISSN: 2329-9290

JOURNAL ARTICLE

Eaton J, Gaubitch ND, Moore AH, Naylor PAet al., 2016, Estimation of Room Acoustic Parameters: The ACE Challenge, IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, Vol: 24, Pages: 1681-1693, ISSN: 2329-9290

JOURNAL ARTICLE

Eaton DJ, Moore AH, Naylor PA, Skoglund Jet al., 2016, Reverberation estimator, US20160118038 A1

Provided are methods and systems for generating Direct-to-Reverberant Ratio (DRR) estimates. The methods and systems use a null-steered beamformer to produce accurate DRR estimates across a variety of room sizes, reverberation times, and source-receiver distances. The DRR estimation algorithm uses spatial selectivity to separate direct and reverberant energy and account for noise separately. The formulation considers the response of the beamformer to reverberant sound and the effect of noise. The DRR estimation algorithm is more robust to background noise than existing approaches, and is applicable where a signal is recorded with two or more microphones, such as with mobile communications devices, laptop computers, and the like.

PATENT

Evers C, Moore AH, Naylor PA, 2016, Localization of Moving Microphone Arrays from Moving Sound Sources for Robot Audition, 24th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 1008-1012, ISSN: 2076-1465

CONFERENCE PAPER

Hafezi S, Moore AH, Naylor PA, 2016, 3D ACOUSTIC SOURCE LOCALIZATION IN THE SPHERICAL HARMONIC DOMAIN BASED ON OPTIMIZED GRID SEARCH, IEEE International Conference on Acoustics, Speech, and Signal Processing, Publisher: IEEE, Pages: 415-419, ISSN: 1520-6149

CONFERENCE PAPER

Hafezi S, Moore AH, Naylor PA, 2016, MULTIPLE SOURCE LOCALIZATION IN THE SPHERICAL HARMONIC DOMAIN USING AUGMENTED INTENSITY VECTORS BASED ON GRID SEARCH, 24th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 602-606, ISSN: 2076-1465

CONFERENCE PAPER

Moore AH, Evers C, Naylor PA, 2016, 2D DIRECTION OF ARRIVAL ESTIMATION OF MULTIPLE MOVING SOURCES USING A SPHERICAL MICROPHONE ARRAY, 24th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 1217-1221, ISSN: 2076-1465

CONFERENCE PAPER

Javed HA, Moore AH, Naylor PA, 2016, SPHERICAL MICROPHONE ARRAY ACOUSTIC RAKE RECEIVERS, IEEE International Conference on Acoustics, Speech, and Signal Processing, Publisher: IEEE, Pages: 111-115, ISSN: 1520-6149

CONFERENCE PAPER

Evers C, Moore AH, Naylor PA, 2016, ACOUSTIC SIMULTANEOUS LOCALIZATION AND MAPPING (A-SLAM) OF A MOVING MICROPHONE ARRAY AND ITS SURROUNDING SPEAKERS, IEEE International Conference on Acoustics, Speech, and Signal Processing, Publisher: IEEE, Pages: 6-10, ISSN: 1520-6149

CONFERENCE PAPER

Javed HA, Moore AH, Naylor PA, 2016, SPHERICAL HARMONIC RAKE RECEIVERS FOR DEREVERBERATION, 15th International Workshop on Acoustic Signal Enhancement (IWAENC), Publisher: IEEE

CONFERENCE PAPER

Moore AH, Naylor PA, 2016, LINEAR PREDICTION BASED DEREVERBERATION FOR SPHERICAL MICROPHONE ARRAYS, 15th International Workshop on Acoustic Signal Enhancement (IWAENC), Publisher: IEEE

CONFERENCE PAPER

Eaton J, Gaubitch ND, Moore AH, Naylor PAet al., 2015, Proceedings of the ACE Challenge Workshop - a satellite event of IEEE-WASPAA (2015)

Several established parameters and metrics have been used to characterize theacoustics of a room. The most important are the Direct-To-Reverberant Ratio(DRR), the Reverberation Time (T60) and the reflection coefficient. Theacoustic characteristics of a room based on such parameters can be used topredict the quality and intelligibility of speech signals in that room.Recently, several important methods in speech enhancement and speechrecognition have been developed that show an increase in performance comparedto the predecessors but do require knowledge of one or more fundamentalacoustical parameters such as the T60. Traditionally, these parameters havebeen estimated using carefully measured Acoustic Impulse Responses (AIRs).However, in most applications it is not practical or even possible to measurethe acoustic impulse response. Consequently, there is increasing researchactivity in the estimation of such parameters directly from speech and audiosignals. The aim of this challenge was to evaluate state-of-the-art algorithmsfor blind acoustic parameter estimation from speech and to promote the emergingarea of research in this field. Participants evaluated their algorithms for T60and DRR estimation against the 'ground truth' values provided with thedata-sets and presented the results in a paper describing the method used.

OTHER

Manning V, Yiallourides C, Brevadt M, Moore A, Auvinet E, Naylor P, Cobb Jet al., 2015, Knee Sounds May Predict Osteoarthritis Severity, Symptoms and Function: Pilot Investigation Toward a Novel Dynamic Imaging System, ARTHRITIS & RHEUMATOLOGY, Vol: 67, ISSN: 2326-5191

JOURNAL ARTICLE

Eaton J, Moore AH, Naylor PA, Skoglund Jet al., 2015, DIRECT-TO-REVERBERANT RATIO ESTIMATION USING A NULL-STEERED BEAMFORMER, 40th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 46-50, ISSN: 1520-6149

CONFERENCE PAPER

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