328 results found
Parada PP, Sharma D, Lainez J, et al., 2014, A QUANTITATIVE COMPARISON OF BLIND C-50 ESTIMATORS, 14th International Workshop on Acoustic Signal Enhancement (IWAENC), Publisher: IEEE, Pages: 298-302
Antonello N, van Waterschoot T, Moonen M, et al., 2014, IDENTIFICATION OF SURFACE ACOUSTIC IMPEDANCES IN A REVERBERANT ROOM USING THE FDTD METHOD, 14th International Workshop on Acoustic Signal Enhancement (IWAENC), Publisher: IEEE, Pages: 114-118
Borges RC, Costa MH, Naylor PA, et al., 2014, Impact of the vent size in the feedback-path and occlusion-effect in hearing aids, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 25-28, ISSN: 2163-4025
Moore AH, Brookes M, Naylor PA, 2013, Roomprints for forensic audio applications, Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), Publisher: IEEE
A roomprint is a quantifiable description of an acoustic environment which can be measured under controlled conditions and estimated from a monophonic recording made in that space. We here identify the properties required of a roomprint in forensic audio applications and review the observable characteristics of a room that, when extracted from recordings, could form the basis of a roomprint. Frequency-dependent reverberation time is investigated as a promising characteristic and used in a room identification experiment giving correct identification in 96% of trials.
Gaubitch N, Brookes M, Naylor P, 2013, Blind Channel Magnitude Response Estimation in Speech using Spectrum Classification, IEEE Transactions on Audio, Speech, and Language Processing, Vol: 21, Pages: 2162-2171, ISSN: 1558-7916
Moore AH, Brookes M, Naylor PA, 2013, Room geometry estimation from a single channel acoustic impulse response, Proc. European Signal Processing Conference (EUSIPCO)
Eaton D, Brookes DM, Naylor PA, 2013, A Comparison of Non-Intrusive SNR Estimation Algorithms and the Use of Mapping Functions, EUSIPCO, Publisher: EURASIP, Pages: 1-5
We present a comparative evaluation of six methods for non-intrusive Signal-to-Noise Ratio (SNR) estimation for narrowband speech in noise. We demonstrate that the performance of all methods can be improved by applying a non-linear mapping function to their estimates of SNR. We have employed phrases built from the TIMIT speech corpus and noises from a broad range of sources including ITU-T P.501, NOISEX-92, and Soundjay. We compare the accuracy of the methods in estimating the SNR of both stationary and non-stationary noise and we conclude that with the mapping function, the best current methods can estimate the SNR to within approximately 3.5 dB for SNRs from -5 dB to 35 dB.
Kowalczyk K, Habets EAP, Kellermann W, et al., 2013, Blind System Identification Using Sparse Learning for TDOA Estimation of Room Reflections, IEEE SIGNAL PROCESSING LETTERS, Vol: 20, Pages: 653-656, ISSN: 1070-9908
Jarrett DP, Habets EAP, Naylor PA, 2013, Spherical harmonic domain noise reduction using an MVDR beamformer and DOA-based second-order statistics estimation, Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)
Eaton J, Gaubitch ND, Naylor PA, 2013, Noise-robust reverberation time estimation using spectral decay distributions with reduced computational cost, Pages: 161-165, ISSN: 1520-6149
Reverberation Time (T60) is an important measure of the acoustic properties of a room. It can provide information about the acoustic environment, the intelligibility, and quality of speech recorded in the room, and help improve the performance of speech processing algorithms with reverberant speech. Where the acoustic impulse response of the room is not available, the T60 must be estimated non-intrusively from reverberant speech. State-of-the-art non-intrusive T60 estimators have been shown to be strongly biased in the presence of noise. We describe a novel T60 estimation algorithm based on spectral decay distributions that provides robustness to additive noise for a range of realistic noise types for signal-to-noise ratios in the range 0 to 35 dB and T60s between 200 and 950 ms. The proposed method also has much reduced computational cost.
Lim F, Thomas MRP, Naylor PA, 2013, MINTFORMER: A SPATIALLY AWARE CHANNEL EQUALIZER, 14th IEEE Workshop on Applications of Signal Processing to AudNew Paltzio and Acoustics (WASPAA), Publisher: IEEE, ISSN: 1931-1168
Lim F, Naylor PA, 2013, ROBUST SPEECH DEREVERBERATION USING SUBBAND MULTICHANNEL LEAST SQUARES WITH VARIABLE RELAXATION, 21st European Signal Processing Conference (EUSIPCO), Publisher: IEEE
Sharma D, Naylor PA, Brookes M, 2013, NON-INTRUSIVE SPEECH INTELLIGIBILITY ASSESSMENT, 21st European Signal Processing Conference (EUSIPCO), Publisher: IEEE
Lim F, Naylor PA, 2013, ROBUST LOW-COMPLEXITY MULTICHANNEL EQUALIZATION FOR DEREVERBERATION, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 689-693, ISSN: 1520-6149
Jarrett DP, Thiergart O, Habets EAP, et al., 2012, Coherence-based diffuseness estimation in the spherical harmonic domain, Proc. of the IEEE Convention of Electrical and Electronics Engineers in Israel (IEEEI)
Jarrett DP, Habets EAP, Benesty J, et al., 2012, A tradeoff beamformer for noise reduction in the spherical harmonic domain, Proc. of the International Workshop on Acoustic Signal Enhancement (IWAENC 2012)
Annibale P, Filos J, Naylor PA, et al., 2012, Geometric inference of the room geometry under temperature variations
Geometric inference is an approach for localizing reflectors in a closed acoustic space. It is based on a simple observation that turns time differences of arrival (TDOA) or time of arrival (TOA) measurements from the signals of a microphone array into a geometric constraint. The reflector localization methodology relies on accurate TDOA which is directly dependent on speed of sound information. Estimating the actual speed of sound at the ambient temperature therefore greatly improves the accuracy of the reflector localization in uncontrolled environments. This manuscript shows how to use the geometric inference jointly with the speed of sound estimation for a more accurate reflector localization. Simulations and experiments show the validity of the proposed approach. © 2012 IEEE.
Drugman T, Thomas MRP, Gudnason J, et al., 2012, Detection of Glottal Closure Instants from Speech Signals: a Quantitative Review, IEEE Trans. Audio Speech Language Proc., Vol: 20, Pages: 994-1006
Lin XS, Khong AWH, Naylor PA, 2012, A Forced Spectral Diversity Algorithm For Speech Dereverberation In The Presence Of Near-common Zeros, IEEE Trans. Audio Speech Language Proc., Vol: 20, Pages: 888-899
Habets EAP, Benesty J, Naylor PA, 2012, Speech Distortion and Interference Rejection Constraint Beamformer, IEEE Trans. Audio Speech Language Proc., Vol: 20, Pages: 854-867
Filos J, Canclini A, Antonacci F, et al., 2012, LOCALIZATION OF PLANAR ACOUSTIC REFLECTORS FROM THE COMBINATION OF LINEAR ESTIMATES, 20th European Signal Processing Conference (EUSIPCO), Publisher: IEEE COMPUTER SOC, Pages: 1019-1023, ISSN: 2076-1465
Sharma D, Naylor PA, Gaubitch ND, et al., 2012, NON INTRUSIVE CODEC IDENTIFICATION ALGORITHM, IEEE International Conference on Acoustics, Speech and Signal Processing, Publisher: IEEE, Pages: 4477-4480, ISSN: 1520-6149
Thomas MRP, Gaubitch ND, Habets EAP, et al., 2012, AN INSIGHT INTO COMMON FILTERING IN NOISY SIMO BLIND SYSTEM IDENTIFICATION, IEEE International Conference on Acoustics, Speech and Signal Processing, Publisher: IEEE, Pages: 521-524, ISSN: 1520-6149
Canclini A, Antonacci F, Filos J, et al., 2012, Exact localization of planar acoustic reflectors in three-dimensional geometries
© 2012, Institute of Electrical and Electronics Engineers Inc. All rights reserved. In this paper we propose a methodology for localizing acoustic planar reflectors in a 3D geometry, using acoustic measurements acquired by a set of microphones. An acoustic source emitting a known signal is placed close to the wall to be identified, and is used for estimating the source-to-microphone impulse responses. In a preliminary step, such estimates are employed for localizing the source. After that, the Times Of Arrival (TOAs) associated to the first order reflective paths are extracted from the impulse responses and converted into quadratic constraints (ellipsoids) acting on the reflective plane. The constraints are then collected into acost function, whose exact minimization leads to the searched plane. A theoretical analysis is performed for predicting the impact of measurement errors on the estimation. Moreover, experimental results in a real meeting room prove the effectiveness of the method.
Sharma D, Hilkhuysen G, Naylor PA, et al., 2012, Descriptive Vocabulary Development for Degraded Speech, 13th Annual Conference of the International-Speech-Communication-Association, Publisher: ISCA-INT SPEECH COMMUNICATION ASSOC, Pages: 1494-1497
Lim F, Naylor PA, 2012, Relaxed multichannel least squares with constrained initial taps for multichannel dereverberation
© 2012, Institute of Electrical and Electronics Engineers Inc. All rights reserved. This paper presents a novel algorithm for robust multichannel dereverberation in the presence of system identification errors with the specific aim of avoiding colouration of the equalized signal. Our proposed algorithm is based upon the technique of channel shortening, which targets only the late taps of the room impulse response. Within the framework of the relaxed multichannel least squares (RMCLS) algorithm, we employ partial relaxation of the early taps of the equalized impulse response (EIR) to increase robustness to channel estimation errors, while constraining the initial taps to avoid undesirable colouration of the equalized signal. It is shown through quantitative experimental results that the resultant equalized signal has an overall improved speech quality perception when compared to alternative algorithms.
Gaubitch ND, Löllmann HW, Jeub M, et al., 2012, Performance comparison of algorithms for blind reverberation time estimation from speech
© 2012, Institute of Electrical and Electronics Engineers Inc. All rights reserved. The reverberation time, T60, is one of the key parameters used to quantify room acoustics. It can provide information about the quality and intelligibility of speech recorded in a reverberant environment, and it can be used to increase robustness to reverberation of speech processing algorithms. T60 can be determined directly from a measurement of the acoustic impulse response, but in situations where this is unavailable it must be estimated blindly from reverberant speech. In this contribution, we provide a study of three state-of-the-art methods for blind T60 estimation. Experimental results with a large number of talkers, simulated and measured acoustic impulse responses, and various levels of additive white Gaussian noise are presented. The relative merits of the three methods in terms of computational time, estimation accuracy, noise sensitivity and inter-talker variance are discussed. In general, all three methods are able to estimate the reverberation time to within 0.2 s for T60 ≤ 0.8 s and SNR ≥ 30 dB, while increasing the noise level causes overestimation. The relative computational speed of the three methods is also assessed.
Naylor PA, Gaubitch ND, 2012, Acoustic signal processing in noise: It's not getting any quieter
© 2012, Institute of Electrical and Electronics Engineers Inc. All rights reserved. Acoustic signal processing research has been addressing the issues associated with additive noise and other degradations in speech for many years and several significant technical advances are now embedded in the state-of-the-art. Nevertheless, the problems are not solved and may actually be worsening. The philosophy advocated in this paper is that further improvements in acoustic signal processing for noise reduction and robustness are, of course, important but are unlikely to be sufficient on their own. Alongside the signal processing, successful systems are likely going to need to include two further factors: an element of matching to the human perception system and also an element of sensing and adaptation to the local environment, giving systems acoustic awareness. Examples of current research on human perception and acoustic signal processing are discussed. These include some aspects of auditory cognition and signal processing methods for building acoustic awareness. A new initiative for benchmarking is also highlighted.
Jarrett D, Habets EAP, Thomas M, et al., 2012, Rigid sphere room impulse response simulation: algorithm and applications, J. Acoust. Soc. America, Vol: 132
Annibale P, Filos J, Naylor PA, et al., 2012, TDOA-based speed of sound estimation for air temperature and room geometry inference, IEEE Trans. Audio, Speech, Lang. Process.
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