Publications
69 results found
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
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.
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.
Eaton DJ, Gaubitch ND, Moore AH, et 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.
Lightburn L, De Sena E, Moore AH, et 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.
Yiallourides C, Manning V, Moore AH, et 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.
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.
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.
Löllmann HW, Moore AH, Naylor PA, et al., 2017, Microphone array signal processing for robot audition, 2017 Hands-free Speech Communications and Microphone Arrays (HSCMA), Publisher: IEEE, Pages: 51-55
Robot audition for humanoid robots interacting naturally with humans in an unconstrained real-world environment is a hitherto unsolved challenge. The recorded microphone signals are usually distorted by background and interfering noise sources (speakers) as well as room reverberation. In addition, the movements of a robot and its actuators cause ego-noise which degrades the recorded signals significantly. The movement of the robot body and its head also complicates the detection and tracking of the desired, possibly moving, sound sources of interest. This paper presents an overview of the concepts in microphone array processing for robot audition and some recent achievements.
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.
Evers C, Moore A, Naylor P, 2016, Localization of Moving Microphone Arrays from Moving Sound Sources for Robot Audition, European Signal Processing Conference (EUSIPCO), Publisher: IEEE, ISSN: 2076-1465
Acoustic Simultaneous Localization and Mapping(a-SLAM) jointly localizes the trajectory of a microphone arrayinstalled on a moving platform, whilst estimating the acousticmap of surrounding sound sources, such as human speakers.Whilst traditional approaches for SLAM in the vision and opticalresearch literature rely on the assumption that the surroundingmap features are static, in the acoustic case the positions oftalkers are usually time-varying due to head rotations and bodymovements. This paper demonstrates that tracking of movingsources can be incorporated in a-SLAM by modelling the acousticmap as a Random Finite Set (RFS) of multiple sources andexplicitly imposing models of the source dynamics. The proposedapproach is verified and its performance evaluated for realisticsimulated data.
Hafezi S, Moore AH, Naylor PA, 2016, Multiple source localization in the spherical harmonic domain using augmented intensity vectors based on grid search, European Signal Processing Conference, Publisher: IEEE, ISSN: 2219-5491
Multiple source localization is an important task in acousticsignal processing with applications including dereverberation,source separation, source tracking and environmentmapping. When using spherical microphone arrays, it hasbeen previously shown that Pseudo-intensity Vectors (PIV),and Augmented Intensity Vectors (AIV), are an effective approachfor direction of arrival estimation of a sound source.In this paper, we evaluate AIV-based localization in acousticscenarios involving multiple sound sources. Simulations areconducted where the number of sources, their angular separationand the reverberation time of the room are varied. Theresults indicate that AIV outperforms PIV and Steered ResponsePower (SRP) with an average accuracy between 5 and10 degrees for sources with angular separation of 30 degreesor more. AIV also shows better robustness to reverberationtime than PIV and SRP.
Moore AH, Evers C, Naylor PA, 2016, 2D direction of arrival estimation of multiple moving sources using a spherical microphone array, European Signal Processing Conference, Publisher: IEEE, ISSN: 2219-5491
Direction of arrival estimation using a spherical microphonearray is an important and growing research area. One promisingalgorithm is the recently proposed Subspace PseudoIntensityVector method. In this contribution the SubspacePseudo-Intensity Vector method is combined with a state-ofthe-artmethod for robustly estimating the centres of mass in a2D histogram based on matching pursuits. The performanceof the improved Subspace Pseudo-Intensity Vector method isevaluated in the context of localising multiple moving sourceswhere it is shown to outperform competing methods in termsof clutter rate and the number of missed detections whilstremaining comparable in terms of localisation accuracy.
Moore AH, Evers C, Naylor PA, 2016, Direction of Arrival Estimation in the Spherical Harmonic Domain using Subspace Pseudo-Intensity Vectors, IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol: 25, Pages: 178-192, ISSN: 2329-9290
Direction of Arrival (DOA) estimation is a fundamental problem in acoustic signal processing. It is used in a diverse range of applications, including spatial filtering, speech dereverberation, source separation and diarization. Intensity vector-based DOA estimation is attractive, especially for spherical sensor arrays, because it is computationally efficient. Two such methods are presented which operate on a spherical harmonic decomposition of a sound field observed using a spherical microphone array. The first uses Pseudo-Intensity Vectors (PIVs) and works well in acoustic environments where only one sound source is active at any time. The second uses Subspace Pseudo-Intensity Vectors (SSPIVs) and is targeted at environments where multiple simultaneous sources and significant levels of reverberation make the problem more challenging. Analytical models are used to quantify the effects of an interfering source, diffuse noise and sensor noise on PIVs and SSPIVs. The accuracy of DOA estimation using PIVs and SSPIVs is compared against the state-of-the-art in simulations including realistic reverberation and noise for single and multiple, stationary and moving sources. Finally, robust performance of the proposed methods is demonstrated using speech recordings in real acoustic environments.
Moore AH, Naylor P, 2016, Linear prediction based dereverberation for spherical microphone arrays, 15th International Workshop on Acoustic Signal Enhancement (IWAENC), Publisher: IEEE
Dereverberation is an important preprocessing step in manyspeech systems, both for human and machine listening. Inmany situations, including robot audition, the sound sourcesof interest can be incident from any direction. In such circumstances,a spherical microphone array allows direction of arrivalestimation which is free of spatial aliasing and directionindependentbeam patterns can be formed. This contributionformulates the Weighted Prediction Error algorithm in thespherical harmonic domain and compares the performance toa space domain implementation. Simulation results demonstratethat performing dereverberation in the spherical harmonicdomain allows many more microphones to be usedwithout increasing the computational cost. The benefit ofusing many microphones is particularly apparent at low signalto noise ratios, where for the conditions tested up to 71%improvement in speech-to-reverberation modulation ratio wasachieved.
Eaton DJ, Gaubitch ND, Moore AH, et al., 2016, Estimation of room acoustic parameters: the ACE challenge, IEEE Transactions on Audio Speech and Language Processing, Vol: 24, Pages: 1681-1693, ISSN: 2329-9290
Reverberation Time (T60) and Direct-to-Reverberant Ratio (DRR) are important parameters which together can characterize sound captured by microphones in non-anechoic rooms. These parameters are important in speech processing applications such as speech recognition and dereverberation. The values of T60 and DRR can be estimated directly from the Acoustic Impulse Response (AIR) of the room. In practice, the AIR isnot normally available, in which case these parameters must be estimated blindly from the observed speech in the microphone signal. The Acoustic Characterization of Environments (ACE) Challenge aimed to determine the state-of-the-art in blind acoustic parameter estimation and also to stimulate research in this area. A summary of the ACE Challenge, and the corpusused in the challenge is presented together with an analysis of the results. Existing algorithms were submitted alongside novel contributions, the comparative results for which are presented in this paper. The challenge showed that T60 estimation is a mature field where analytical approaches dominate whilst DRR estimation is a less mature field where machine learning approaches are currently more successful.
Eaton DJ, Moore AH, Naylor PA, et 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.
Javed HA, Moore AH, Naylor PA, 2016, Spherical microphone array acoustic rake receivers, ICASSP, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, Publisher: IEEE, Pages: 111-115, ISSN: 0736-7791
Several signal independent acoustic rake receivers are proposed for speech dereverberation using spherical microphone arrays. The proposed rake designs take advantage of multipaths, by separately capturing and combining early reflections with the direct path. We investigate several approaches in combining reflections with the direct path source signal, including the development of beam patterns that point nulls at all preceding reflections. The proposed designs are tested in experimental simulations and their dereverberation performances evaluated using objective measures. For the tested configuration, the proposed designs achieve higher levels of dereverberation compared to conventional signal independent beamforming systems; achieving up to 3.6 dB improvement in the direct-to-reverberant ratio over the plane-wave decomposition beamformer.
Evers C, Moore AH, Naylor PA, 2016, Acoustic simultaneous localization and mapping (A-SLAM) of a moving microphone array and its surrounding speakers, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 6-10, ISSN: 1520-6149
Acoustic scene mapping creates a representation of positions of audio sources such as talkers within the surrounding environment of a microphone array. By allowing the array to move, the acoustic scene can be explored in order to improve the map. Furthermore, the spatial diversity of the kinematic array allows for estimation of the source-sensor distance in scenarios where source directions of arrival are measured. As sound source localization is performed relative to the array position, mapping of acoustic sources requires knowledge of the absolute position of the microphone array in the room. If the array is moving, its absolute position is unknown in practice. Hence, Simultaneous Localization and Mapping (SLAM) is required in order to localize the microphone array position and map the surrounding sound sources. In realistic environments, microphone arrays receive a convolutive mixture of direct-path speech signals, noise and reflections due to reverberation. A key challenge of Acoustic SLAM (a-SLAM) is robustness against reverberant clutter measurements and missing source detections. This paper proposes a novel bearing-only a-SLAM approach using a Single-Cluster Probability Hypothesis Density filter. Results demonstrate convergence to accurate estimates of the array trajectory and source positions.
Evers C, Moore A, Naylor P, 2016, Towards Informative Path Planning for Acoustic SLAM, DAGA 2016
Acoustic scene mapping is a challenging task as microphonearrays can often localize sound sources only interms of their directions. Spatial diversity can be exploitedconstructively to infer source-sensor range whenusing microphone arrays installed on moving platforms,such as robots. As the absolute location of a moving robotis often unknown in practice, Acoustic SimultaneousLocalization And Mapping (a-SLAM) is required in orderto localize the moving robot’s positions and jointlymap the sound sources. Using a novel a-SLAM approach,this paper investigates the impact of the choice of robotpaths on source mapping accuracy. Simulation results demonstratethat a-SLAM performance can be improved byinformatively planning robot paths.
Hafezi S, Moore AH, Naylor PA, 2016, 3D ACOUSTIC SOURCE LOCALIZATION IN THE SPHERICAL HARMONIC DOMAIN BASED ON OPTIMIZED GRID SEARCH, 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 415-419, ISSN: 1520-6149
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Javed HA, Moore AH, Naylor PA, 2016, SPHERICAL HARMONIC RAKE RECEIVERS FOR DEREVERBERATION, 15th International Workshop on Acoustic Signal Enhancement (IWAENC), Publisher: IEEE
Manning V, Yiallourides C, Brevadt M, et 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
Eaton J, Gaubitch ND, Moore AH, et al., 2015, Proceedings of the ACE Challenge Workshop, a satellite event of IEEE-WASPAA
Several established parameters and metrics have been used to characterize the acoustics of a room. The most important are the Direct-To-Reverberant Ratio (DRR), the Reverberation Time (T60) and the reflection coefficient. The acoustic characteristics of a room based on such parameters can be used to predict the quality and intelligibility of speech signals in that room. Recently, several important methods in speech enhancement and speech recognition have been developed that show an increase in performance compared to the predecessors but do require knowledge of one or more fundamental acoustical parameters such as the T60. Traditionally, these parameters have been estimated using carefully measured Acoustic Impulse Responses (AIRs). However, in most applications it is not practical or even possible to measure the acoustic impulse response. Consequently, there is increasing research activity in the estimation of such parameters directly from speech and audio signals. The aim of this challenge was to evaluate state-of-the-art algorithms for blind acoustic parameter estimation from speech and to promote the emerging area of research in this field. Participants evaluated their algorithms for T60 and DRR estimation against the ’ground truth’ values provided with the data-sets and presented the results in a paper describing the method used.
Eaton J, Gaubitch ND, Moore AH, et al., 2015, The ACE Challenge - corpus description and performance evaluation, WASPAA, Publisher: IEEE
Knowledge of the Direct-to-Reverberant Ratio (DRR) and Reverberation Time (T60) can be used to better perform speech and audio processing such as dereverberation. Established methods compute these parameters from measured Acoustic Impulse Responses (AIRs). However, in many practical situations the AIR is not available and the parameters must be estimated non-intrusively directly from noisy speech or audio signals. The Acoustic Characterization of Environments (ACE) Challenge is a competition to identify the most promising non-intrusive DRR and T60 estimation methods using real noisy reverberant speech. We describe the ACE corpus comprising multi-channel AIRs, and multi-channel noise including ambient, fan and babble noise recorded in the same environment as the measured AIRs, along with the corresponding DRR and T60 measurements. The evaluation methodology is discussed and comparative results are shown.
Moore AH, Evers C, Naylor PA, et al., 2015, Direction of arrival estimation using pseudo-intensity vectors with direct-path dominance test, European Signal Processing Conference, Publisher: IEEE, Pages: 2296-2300, ISSN: 2219-5491
The accuracy of direction of arrival estimation tends to degrade under reverberant conditions due to the presence of reflected signal components which are correlated with the direct path. The recently proposed direct-path dominance test provides a means of identifying time-frequency regions in which a single signal path is dominant. By analysing only these regions it was shown that the accuracy of the FS-MUSIC algorithm could be significantly improved. However, for real-time implementation a less computationally demanding localisation algorithm would be preferable. In the present contribution we investigate the direct-path dominance test as a preprocessing step to pseudo-intensity vector-based localisation. A novel formulation of the pseudo-intensity vector is proposed which further exploits the direct path dominance test and leads to improved localisation performance.
Evers C, Moore AH, Naylor PA, et al., 2015, Bearing-only acoustic tracking of moving speakers for robot audition, 2015 IEEE International Conference on Digital Signal Processing (DSP), Publisher: IEEE, Pages: 1206-1210, ISSN: 1546-1874
This paper focuses on speaker tracking in robot audition for human-robot interaction. Using only acoustic signals, speaker tracking in enclosed spaces is subject to missing detections and spurious clutter measurements due to speech inactivity, reverberation and interference. Furthermore, many acoustic localization approaches estimate speaker direction, hence providing bearing-only measurements without range information. This paper presents a probability hypothesis density (PHD) tracker that augments the bearing-only speaker directions of arrival with a cloud of range hypotheses at speaker initiation and propagates the random variates through time. Furthermore, due to their formulation PHD filters explicitly model, and hence provide robustness against, clutter and missing detections. The approach is verified using experimental results.
Moore AH, Evers C, Naylor PA, 2015, Multichannel equalisation for high-order spherical microphone arrays using beamformed channels, 2015 IEEE International Conference on Digital Signal Processing (DSP), Publisher: IEEE, Pages: 1211-1215, ISSN: 1546-1874
High-order spherical microphone arrays offer many practical benefits including relatively fine spatial resolution in all directions and rotation invariant processing using eigenbeams. Spatial filtering can reduce interference from noise and reverberation but in even moderately reverberant environments the beam pattern fails to suppress reverberation to a level adequate for typical applications. In this paper we investigate the feasibility of applying dereverberation by considering multiple beamformer outputs as channels to be dereverberated. In one realisation we process directly in the spherical harmonic domain where the beampatterns are mutually orthogonal. In a second realisation, which is not limited to spherical microphone arrays, beams are pointed in the direction of dominant reflections. Simulations demonstrate that in both cases reverberation is significantly reduced and, in the best case, clarity index is improved by 15 dB.
Eaton J, Moore AH, Naylor PA, et al., 2015, Direct-to-reverberant ratio estimation using a null-steered beamformer, ICASSP, Publisher: IEEE, Pages: 46-50
Reverberation affects the quality and intelligibility of distant speech recorded in a room. Direct-to-Reverberant Ratio (DRR) is a useful measure for assessing the acoustic configuration and can be used to inform dereverberation algorithms. We describe a novel DRR estimation algorithm applicable where the signal was recorded with two or more microphones, such as mobile communications devices and laptops. The method uses a null-steered beamformer. In simulations the proposed method yields accurate DRR estimates to within +/- 4 dB across a across a wide variety of room sizes, reverberation times and source-receiver distances. It is also shown that the proposed method is more robust to background noise than a baseline approach. The best estimation accuracy is obtained in the region from -5 to 5 dB which is a relevant range for portable devices.
Hafezi S, Moore AH, Naylor PA, 2015, MODELING SOURCE DIRECTIVITY IN ROOM IMPULSE RESPONSE SIMULATION FOR SPHERICAL MICROPHONE ARRAYS, 23rd European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 574-578, ISSN: 2076-1465
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