29,933 research outputs found
Articulatory based speech models for blind speech dereverberation using sequential Monte Carlo methods
Full text linkPublication in the conference proceedings of EUSIPCO, Aalborg, Denmark, 201
Block-based TVAR models for single-channel blind dereverberation of speech from a moving speaker
No full textIn reverberant environments, a moving speaker yields a dynamically changing source-sensor geometry giving rise to a spatially-varying acoustic impulse response (AIR) between the source and sensor. Consequently, this leads to a time-varying convolutional relationship between the source signal and the observations and thus spectral colouration of the received signal. It is therefore desirable to reduce the effect of reverberation. In this paper, a model-based approach is proposed for single-channel blind dereverberation of speech from a moving speaker acquired in an acoustic environment. The sound source is modelled by a block-based time-varying AR (TVAR) process, and the channel by a linear time-varying all-pole filter. In each case, the AR parameters are represented as a linear combination of known basis functions with unknown weightings. The speech model captures local nonstationarity while taking account of the global nonstationary characteristics inherent in long segments of speech. As an initial step towards single-channel blind dereverberation of real speech signals, this paper presents simulation results for synthetic data to demonstrate the algorithm developed.</p
Parametric modelling for single-channel blind dereverberation of speech from a moving speaker
Full text linkSingle-channel blind dereverberation for the enhancement of speech acquired in acoustic environments is essential in applications where microphone arrays prove impractical. In many scenarios, the source-sensor geometry is not varying rapidly, but in most applications the geometry is subject to change, for example when a user wishes to move around a room. A previous model-based approach to blind dereverberation by representing the channel as a linear time-varying all-pole filter is extended, in which the parameters of the filter are modelled as a linear combination of known basis functions with unknown weightings. Moreover, an improved block-based time-varying autoregressive model is proposed for the speech signal, which aims to reflect the underlying signal statistics more accurately on both a local and global level. Given these parametric models, their coefficients are estimated using Bayesian inference, so that the channel estimate can then be used for dereverberation. An in-depth discussion is also presented about the applicability of these models to real speech and a real acoustic environment. Results are presented to demonstrate the performance of the Bayesian inference algorithms
Multichannel Online Blind Speech Dereverberation with Marginalization of Static Observation Parameters in a Rao-Blackwellized Particle Filter
Full text linkRoom reverberation leads to reduced intelligibility of audio signals and spectral coloration of audio signals. Enhancement of acoustic signals is thus crucial for high-quality audio and scene analysis applications. Multiple sensors can be used to exploit statistical evidence from multiple observations of the same event to improve enhancement. Whilst traditional beamforming techniques suffer from interfering reverberant reflections with the beam path, other approaches to dereverberation often require at least partial knowledge of the room impulse response which is not available in practice, or rely on inverse filtering of a channel estimate to obtain a clean speech estimate, resulting in difficulties with non-minimum phase acoustic impulse responses. This paper proposes a multi-sensor approach to blind dereverberation in which both the source signal and acoustic channel are directly estimated from the distorted observations using their optimal estimators. The remaining model parameters are sampled from hypothesis distributions using a particle filter, thus facilitating real-time dereverberation. This approach was previously successfully applied to single-sensor blind dereverberation. In this paper, the single-channel approach is extended to multiple sensors. Performance improvements due to the use of multiple sensors are demonstrated on synthetic and baseband speech examples.</p
Approximate Adaptive Beam-Pattern Design
No full textMatlab code supporting the publication: Herbert, Hopgood and Mulgrew, "Computationally simple MMSE (A-optimal)adaptive beam-pattern design for MIMO activesensing systems via a linear-Gaussian approximation", IEEE Transactions on Signal Processing (in submission)
Marginalization of static observation parameters in a rao-blackwellized particle filter with application to sequential blind speech dereverberation
No full textEnhancement of an unknown signal from distorted observations is an extremely important Engineering problem. In addition to noise, the observation space often contains a degrading filter component. A typical example is blind speech enhancement, where a reverberant channel between a stationary source and the receiver can be modeled as a static infinite impulse response component. Particle filters have become popular and versatile estimators for estimating the clean source signal and unknown model parameters by sequentially drawing a large number of samples from a hypothesis distribution. However, direct sampling of static components leads to particle impoverishment as a dynamic is implicitly enforced on the parameters. To circumvent this issue, this paper proposes a novel approach by exploiting analytically tractable substructures of the state space to marginalize static components, facilitating separate estimation of the static parameters using their optimal estimator. The approach is tested for blind dereverberation of speech. Results show that the proposed algorithm effectively removes the effects of the static reverberant channel.</p
Software for "Approximate Adaptive Beam-Pattern Design"
No full textMatlab code supporting the publication: Herbert, Hopgood and Mulgrew, "Computationally simple MMSE (A-optimal)adaptive beam-pattern design for MIMO activesensing systems via a linear-Gaussian approximation", accepted in IEEE Transactions on Signal Processing.Herbert, Steven; Hopgood, James R.; Mulgrew, Bernie. (2018). Approximate Adaptive Beam-Pattern Design, [software]. University of Edinburgh. School of Engineering. Institute of Digital Communications. https://doi.org/10.7488/ds/2403
Multi-beam Sonar Image Sequences
No full textThe dataset supports the following article: Diamantis K., Yaghoobi M., Davies M. and Hopgood J. 2019, 'A multi-frame resolution enhancement framework for multi-beam sonar systems' submitted for review to the IEEE Transactions of Geoscience and Remote Sensing. The article proposes a framework for the improvement of multi-beam echo-sounder image quality with the objective to facilitate real-time high-resolution applications. The framework relies on the fact that a large number of images are commonly acquired from different positions, during underwater scans. The dataset consists of seven different multi-beam sonar image sequences, used in the studies of improving sonar image resolution. The image sequences depict various underwater objects/structures and are converted to video-clips. Each video-clip includes from left to right: the standard sonar images, the images resulting from image registration and averaging, and the reconstructed images using the proposed high-resolution framework. All images have been processed using Matlab (The MathWorks, Inc., Natick, MA, USA) scripts
Blind speech dereverberation using batch and sequential Monte Carlo methods
No full textReverberation and noise cause significant deterioration of audio quality and intelligibility to signals recorded in acoustic environments. Bayesian dereverberation infers knowledge about the system by exploiting the statistical properties of speech and the acoustic channel. In Bayesian frameworks, the signal can be processed either sequentially using online methods or in a batch using offline methods. This paper compares the two approaches for blind speech dereverberation by means of a previously proposed batch approach and a novel sequential approach. Results show that while both methods have different advantages, online processing leads to a more flexible solution. ©2008 IEEE.</p
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