Publications
443 results found
De Sena E, Brookes DM, Naylor PA, et al., 2017, Localization Experiments with Reporting by Head Orientation: Statistical Framework and Case Study, 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.
Weiss S, Goddard NJ, Somasundaram S, et al., 2017, Identification of Broadband Source-Array Responses from Sensor Second Order Statistics, Sensor Signal Processing for Defence Conference (SSPD), Publisher: IEEE, Pages: 35-39
This paper addresses the identification of source-sensor transfer functions from the measured space-time covariance matrix in the absence of any further side information about the source or the propagation environment. Using polynomial matrix decomposition techniques, the responses can be narrowed down to an indeterminacy of a common polynomial factor. If at least two different measurements for a source with constant power spectral density are available, this indeterminacy can be reduced to an ambiguity in the phase response of the source-sensor paths.
Papayiannis C, Evers C, Naylor PA, 2017, Sparse parametric modeling of the early part of acoustic impulse responses, 25th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 678-682, ISSN: 2076-1465
Acoustic channels are typically described by their Acoustic Impulse Response (AIR) as a Moving Average (MA) process. Such AIRs are often considered in terms of their early and late parts, describing discrete reflections and the diffuse reverberation tail respectively. We propose an approach for constructing a sparse parametric model for the early part. The model aims at reducing the number of parameters needed to represent it and subsequently reconstruct from the representation the MA coefficients that describe it. It consists of a representation of the reflections arriving at the receiver as delayed copies of an excitation signal. The Time-Of-Arrivals of reflections are not restricted to integer sample instances and a dynamically estimated model for the excitation sound is used. We also present a corresponding parameter estimation method, which is based on regularized-regression and nonlinear optimization. The proposed method also serves as an analysis tool, since estimated parameters can be used for the estimation of room geometry, the mixing time and other channel properties. Experiments involving simulated and measured AIRs are presented, in which the AIR coefficient reconstruction-error energy does not exceed 11.4% of the energy of the original AIR coefficients. The results also indicate dimensionality reduction figures exceeding 90% when compared to a MA process representation.
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.
Antonello N, De Sena E, Moonen M, et al., 2017, Room Impulse Response Interpolation Using a Sparse Spatio-Temporal Representation of the Sound Field, IEEE/ACM Transactions on Audio Speech and Language Processing, Vol: 25, Pages: 1929-1941, ISSN: 2329-9290
© 2017 IEEE. Room Impulse Responses (RIRs) are typically measured using a set of microphones and a loudspeaker. When RIRs spanning a large volume are needed, many microphone measurements must be used to spatially sample the sound field. In order to reduce the number of microphone measurements, RIRs can be spatially interpolated. In the present study, RIR interpolation is formulated as an inverse problem. This inverse problem relies on a particular acoustic model capable of representing the measurements. Two different acoustic models are compared: the plane wave decomposition model and a novel time-domain model, which consists of a collection of equivalent sources creating spherical waves. These acoustic models can both approximate any reverberant sound field created by a far-field sound source. In order to produce an accurate RIR interpolation, sparsity regularization is employed when solving the inverse problem. In particular, by combining different acoustic models with different sparsity promoting regularizations, spatial sparsity, spatio-spectral sparsity, and spatio-temporal sparsity are compared. The inverse problem is solved using a matrix-free large-scale optimization algorithm. Simulations show that the best RIR interpolation is obtained when combining the novel time-domain acoustic model with the spatio-temporal sparsity regularization, outperforming the results of the plane wave decomposition model even when far fewer microphone measurements are available.
Parada PP, Sharma D, van Waterschoot T, et al., 2017, Robust Statistical Processing of TDOA Estimates for Distant Speaker Diarization, 25th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 86-90, ISSN: 2076-1465
Sharma D, Jost U, Naylor PA, 2017, Non-Intrusive Bit-Rate Detection of Coded Speech, 25th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 1799-1803, ISSN: 2076-1465
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.
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.
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.
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.
Papayiannis C, Evers C, Naylor PA, 2017, Discriminative feature domains for reverberant acoustic environments, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, ISSN: 2379-190X
Several speech processing and audio data-mining applicationsrely on a description of the acoustic environment as a featurevector for classification. The discriminative properties of thefeature domain play a crucial role in the effectiveness of thesemethods. In this work, we consider three environment iden-tification tasks and the task of acoustic model selection forspeech recognition. A set of acoustic parameters and Ma-chine Learning algorithms for feature selection are used andan analysis is performed on the resulting feature domains foreach task. In our experiments, a classification accuracy of100% is achieved for the majority of tasks and the Word Er-ror Rate is reduced by 20.73 percentage points for AutomaticSpeech Recognition when using the resulting domains. Ex-perimental results indicate a significant dissimilarity in theparameter choices for the composition of the domains, whichhighlights the importance of the feature selection process forindividual applications.
Evers C, Dorfan Y, Gannot S, et al., 2017, Source tracking using moving microphone arrays for robot audition, IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE
Intuitive spoken dialogues are a prerequisite for human-robot inter-action. In many practical situations, robots must be able to identifyand focus on sources of interest in the presence of interfering speak-ers. Techniques such as spatial filtering and blind source separa-tion are therefore often used, but rely on accurate knowledge of thesource location. In practice, sound emitted in enclosed environmentsis subject to reverberation and noise. Hence, sound source localiza-tion must be robust to both diffuse noise due to late reverberation, aswell as spurious detections due to early reflections. For improvedrobustness against reverberation, this paper proposes a novel ap-proach for sound source tracking that constructively exploits the spa-tial diversity of a microphone array installed in a moving robot. Inprevious work, we developed speaker localization approaches usingexpectation-maximization (EM) approaches and using Bayesian ap-proaches. In this paper we propose to combine the EM and Bayesianapproach in one framework for improved robustness against rever-beration and noise.
Xue W, Brookes M, Naylor PA, 2017, Frequency-domain under-modelled blind system identification based on cross power spectrum and sparsity regularization, IEEE International Conference on Acoustics, Speech and Signal Processing, Pages: 591-595, ISSN: 1520-6149
© 2017 IEEE. In room acoustics, under-modelled multichannel blind system identification (BSI) aims to estimate the early part of the room impulse responses (RIRs), and it can be widely used in applications such as speaker localization, room geometry identification and beamforming based speech dereverberation. In this paper we extend our recent study on under-modelled BSI from the time domain to the frequency domain, such that the RIRs can be updated frame-wise and the efficiency of Fast Fourier Transform (FFT) is exploited to reduce the computational complexity. Analogous to the cross-correlation based criterion in the time domain, a frequency-domain cross power spectrum based criterion is proposed. As the early RIRs are usually sparse, the RIRs are estimated by jointly maximizing the cross power spectrum based criterion in the frequency domain and minimizing the l 1 -norm sparsity measure in the time domain. A two-stage LMS updating algorithm is derived to achieve joint optimization of these two targets. The experimental results in different under-modelled scenarios demonstrate the effectiveness of the proposed method.
Eaton DJ, javed HA, Naylor PA, 2017, Estimation of the perceived level of reverberation using non-intrusive single-channel variance of decay rates, Joint Workshop on Hands-free Speech Communication and Microphone Arrays, Publisher: IEEE
The increasing processing power of hearing aids and mobile deviceshas led to the potential for incorporation of dereverberation algorithms to improve speech quality for the listener. Assessing the effectiveness of deverberation algorithms using subjective listening tests is extremely time consuming and depends on averaging out listener variations over a large number of subjects. Also, most existing instrumental measures are intrusive and require knowledge of the original signal which precludes many practical applications. In this paper we show that the proposed non-intrusive single-channel algorithm is a predictor of the perceived level of reverberation thatcorrelates well with subjective listening test results, outperforming many existing intrusive and non-intrusive measures. The algorithm requires only a single training step and has a very low computational complexity making it suitable for hearing aids and mobile telephone applications. The source code has been made freely available.
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.
Gebru ID, Evers C, Naylor PA, et al., 2017, Audio-visual tracking by density approximation in a sequential Bayesian filtering framework, HSCMA 2017, Publisher: IEEE, Pages: 71-75
This paper proposes a novel audio-visual tracking approach that exploits constructively audio and visual modalities in order to estimate trajectories of multiple people in a joint state space. The tracking problem is modeled using a sequential Bayesian filtering framework. Within this framework, we propose to represent the posterior density with a Gaussian Mixture Model (GMM). To ensure that a GMM representation can be retained sequentially over time, the predictive density is approximated by a GMM using the Unscented Transform. While a density interpolation technique is introduced to obtain a continuous representation of the observation likelihood, which is also a GMM. Furthermore, to prevent the number of mixtures from growing exponentially over time, a density approximation based on the Expectation Maximization (EM) algorithm is applied, resulting in a compact GMM representation of the posterior density. Recordings using a camcorder and microphone array are used to evaluate the proposed approach, demonstrating significant improvements in tracking performance of the proposed audio-visual approach compared to two benchmark visual trackers.
Doire CSJ, Brookes DM, Naylor PA, 2017, Robust and efficient Bayesian adaptive psychometric function estimation, Journal of the Acoustical Society of America, Vol: 141, Pages: 2501-2512, ISSN: 0001-4966
The efficient measurement of the threshold and slope of the psychometric function (PF) is an important objective in psychoacoustics. This paper proposes a procedure that combines a Bayesian estimate of the PF with either a look one-ahead or a look two-ahead method of selecting the next stimulus presentation. The procedure differs from previously proposed algorithms in two respects: (i) it does not require the range of possible PF parameters to be specified in advance and (ii) the sequence of probe signal-to-noise ratios optimizes the threshold and slope estimates at a performance level, ϕ, that can be chosen by the experimenter. Simulation results show that the proposed procedure is robust and that the estimates of both threshold and slope have a consistently low bias. Over a wide range of listener PF parameters, the root-mean-square errors after 50 trials were ∼1.2 dB in threshold and 0.14 in log-slope. It was found that the performance differences between the look one-ahead and look two-ahead methods were negligible and that an entropy-based criterion for selecting the next stimulus was preferred to a variance-based criterion.
Pinero G, Naylor PA, 2017, CHANNEL ESTIMATION FOR CROSSTALK CANCELLATION IN WlRELESS ACOUSTIC NETWORKS, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 586-590, ISSN: 1520-6149
Javed HA, Cauchi B, Doclo S, et al., 2017, MEASURING, MODELLING AND PREDICTING PERCEIVED REVERBERATION, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 381-385, ISSN: 1520-6149
Doire CSJ, Brookes DM, Naylor PA, et al., 2017, Single-channel online enhancement of speech corrupted by reverberation and noise, IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol: 25, Pages: 572-587, ISSN: 2329-9290
This paper proposes an online single-channel speech enhancement method designed to improve the quality of speech degraded by reverberation and noise. Based on an autoregressive model for the reverberation power and on a hidden Markov model for clean speech production, a Bayesian filtering formulation of the problem is derived and online joint estimation of the acoustic parameters and mean speech, reverberation, and noise powers is obtained in mel-frequency bands. From these estimates, a real-valued spectral gain is derived and spectral enhancement is applied in the short-time Fourier transform (STFT) domain. The method yields state-of-the-art performance and greatly reduces the effects of reverberation and noise while improving speech quality and preserving speech intelligibility in challenging acoustic environments.
Parada PP, Sharma D, van Waterschoot T, et al., 2017, Confidence Measures for Nonintrusive Estimation of Speech Clarity Index, JOURNAL OF THE AUDIO ENGINEERING SOCIETY, Vol: 65, Pages: 90-99, ISSN: 1549-4950
Evers C, Rafaely B, Naylor PA, 2017, Speaker tracking in reverberant environments using multiple detections of arrival, HSCMA 2017, Publisher: IEEE
Accurate estimation of the Direction of Arrival (DOA) of a soundsource is an important prerequisite for a wide range of acoustic sig-nal processing applications. However, in enclosed environments,early reflections and late reverberation often lead to localization er-rors. Recent work demonstrated that improved robustness againstreverberation can be achieved by clustering only the DOAs fromdirect-path bins in the short-term Fourier transform of a speech sig-nal of several seconds duration from a static talker. Nevertheless, formoving talkers, short blocks of at most several hundred millisecondsare required to capture the spatio-temporal variation of the sourcedirection. Processing of short blocks of data in reverberant envi-ronment can lead to clusters whose centroids correspond to spuri-ous DOAs away from the source direction. We therefore propose inthis paper a novel multi-detection source tracking approach that es-timates the smoothed trajectory of the source DOAs. Results for re-alistic room simulations validate the proposed approach and demon-strate significant improvements in estimation accuracy compared tosingle-detection tracking.
Jarrett DP, Habets EAP, Naylor PA, 2017, Introduction, Springer Topics in Signal Processing, Pages: 1-10
The motivation behind this book lies in the rapidly growing interest in spherical microphone arrays over the last decade. Important applications for these arrays include human-human and human-machine speech communication systems and spatial sound recording. While human-human speech communication systems have a long history, speech also plays an ever-growing part in human-machine communication. This trend has been fuelled by advances in speech recognition technology, as well as the explosion in available computing power, particularly on mobile devices. With the widespread availability of 3D sound cinema systems and virtual reality gear with 3D binaural sound reproduction, the need to capture spatial sound is rapidly growing. Spherical microphone arrays are particularly suitable for capturing all three dimensions of the sound field, including both ambient sounds and sounds from particular directions. In this chapter, we introduce the topic of acoustic signal processing using microphone arrays, and then explore spherical microphone arrays in more detail. We provide an outline of the structure of the book, and discuss the relationships between each of the subsequent chapters.
Jarrett DP, Habets EAP, Naylor PA, 2017, Signal-Dependent Array Processing, THEORY AND APPLICATIONS OF SPHERICAL MICROPHONE ARRAY PROCESSING, Publisher: SPRINGER-VERLAG BERLIN, Pages: 113-139, ISBN: 978-3-319-42209-1
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Jarrett DP, Habets EAP, Naylor PA, 2017, Spherical Array Acoustic Impulse Response Simulation, THEORY AND APPLICATIONS OF SPHERICAL MICROPHONE ARRAY PROCESSING, Publisher: SPRINGER-VERLAG BERLIN, Pages: 39-64, ISBN: 978-3-319-42209-1
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