266 research outputs found
Binaural Speech Enhancement Using STOI-Optimal Masks
STOI-optimal masking has been previously proposed and developed for
single-channel speech enhancement. In this paper, we consider the extension to
the task of binaural speech enhancement in which spatial information is known
to be important to speech understanding and therefore should be preserved by
the enhancement processing. Masks are estimated for each of the binaural
channels individually and a `better-ear listening' mask is computed by choosing
the maximum of the two masks. The estimated mask is used to supply probability
information about the speech presence in each time-frequency bin to an
Optimally-modified Log Spectral Amplitude (OM-LSA) enhancer. We show that using
the proposed method for binaural signals with a directional noise not only
improves the SNR of the noisy signal but also preserves the binaural cues and
intelligibility.Comment: Accepted at IWAENC 202
Measuring audio-visual speech intelligibility under dynamic listening conditions using virtual reality
The ELOSPHERES project is a collaboration between researchers at Imperial College London and University College London which aims to improve the efficacy of hearing aids. The benefit obtained from hearing aids varies significantly between listeners and listening environments. The noisy, reverberant environments which most people find challenging bear little resemblance to the clinics in which consultations occur. In order to make progress in speech enhancement, algorithms need to be evaluated under realistic listening conditions. A key aim of ELOSPHERES is to create a virtual reality-based test environment in which alternative speech enhancement algorithms can be evaluated using a listener-in-the-loop paradigm. In this paper we present the sap-elospheres-audiovisual-test (SEAT) platform and report the results of an initial experiment in which it was used to measure the benefit of visual cues in a speech intelligibility in spatial noise task
Speech Separation Using Partially Asynchronous Microphone Arrays Without Resampling
We consider the problem of separating speech sources captured by multiple
spatially separated devices, each of which has multiple microphones and samples
its signals at a slightly different rate. Most asynchronous array processing
methods rely on sample rate offset estimation and resampling, but these offsets
can be difficult to estimate if the sources or microphones are moving. We
propose a source separation method that does not require offset estimation or
signal resampling. Instead, we divide the distributed array into several
synchronous subarrays. All arrays are used jointly to estimate the time-varying
signal statistics, and those statistics are used to design separate
time-varying spatial filters in each array. We demonstrate the method for
speech mixtures recorded on both stationary and moving microphone arrays.Comment: To appear at the International Workshop on Acoustic Signal
Enhancement (IWAENC 2018
Engineering data compendium. Human perception and performance. User's guide
The concept underlying the Engineering Data Compendium was the product of a research and development program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design and military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from the existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by systems designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is the first volume, the User's Guide, containing a description of the program and instructions for its use
Informed algorithms for sound source separation in enclosed reverberant environments
While humans can separate a sound of interest amidst a cacophony of contending sounds in an echoic environment, machine-based methods lag behind in solving this task. This thesis thus aims at improving performance of audio separation algorithms when they are informed i.e. have access to source location information. These locations are assumed to be known a priori in this work, for example by video processing.
Initially, a multi-microphone array based method combined with binary
time-frequency masking is proposed. A robust least squares frequency invariant data independent beamformer designed with the location information is
utilized to estimate the sources. To further enhance the estimated sources, binary time-frequency masking based post-processing is used but cepstral domain smoothing is required to mitigate musical noise.
To tackle the under-determined case and further improve separation performance
at higher reverberation times, a two-microphone based method
which is inspired by human auditory processing and generates soft time-frequency masks is described. In this approach interaural level difference,
interaural phase difference and mixing vectors are probabilistically modeled in the time-frequency domain and the model parameters are learned
through the expectation-maximization (EM) algorithm. A direction vector is estimated for each source, using the location information, which is used as
the mean parameter of the mixing vector model. Soft time-frequency masks are used to reconstruct the sources. A spatial covariance model is then integrated into the probabilistic model framework that encodes the spatial
characteristics of the enclosure and further improves the separation performance
in challenging scenarios i.e. when sources are in close proximity and
when the level of reverberation is high.
Finally, new dereverberation based pre-processing is proposed based on the cascade of three dereverberation stages where each enhances the twomicrophone
reverberant mixture. The dereverberation stages are based on amplitude spectral subtraction, where the late reverberation is estimated and suppressed. The combination of such dereverberation based pre-processing and use of soft mask separation yields the best separation performance. All methods are evaluated with real and synthetic mixtures formed for example from speech signals from the TIMIT database and measured room impulse responses
An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation
Speech enhancement and speech separation are two related tasks, whose purpose
is to extract either one or more target speech signals, respectively, from a
mixture of sounds generated by several sources. Traditionally, these tasks have
been tackled using signal processing and machine learning techniques applied to
the available acoustic signals. Since the visual aspect of speech is
essentially unaffected by the acoustic environment, visual information from the
target speakers, such as lip movements and facial expressions, has also been
used for speech enhancement and speech separation systems. In order to
efficiently fuse acoustic and visual information, researchers have exploited
the flexibility of data-driven approaches, specifically deep learning,
achieving strong performance. The ceaseless proposal of a large number of
techniques to extract features and fuse multimodal information has highlighted
the need for an overview that comprehensively describes and discusses
audio-visual speech enhancement and separation based on deep learning. In this
paper, we provide a systematic survey of this research topic, focusing on the
main elements that characterise the systems in the literature: acoustic
features; visual features; deep learning methods; fusion techniques; training
targets and objective functions. In addition, we review deep-learning-based
methods for speech reconstruction from silent videos and audio-visual sound
source separation for non-speech signals, since these methods can be more or
less directly applied to audio-visual speech enhancement and separation.
Finally, we survey commonly employed audio-visual speech datasets, given their
central role in the development of data-driven approaches, and evaluation
methods, because they are generally used to compare different systems and
determine their performance
Audio source separation into the wild
International audienceThis review chapter is dedicated to multichannel audio source separation in real-life environment. We explore some of the major achievements in the field and discuss some of the remaining challenges. We will explore several important practical scenarios, e.g. moving sources and/or microphones, varying number of sources and sensors, high reverberation levels, spatially diffuse sources, and synchronization problems. Several applications such as smart assistants, cellular phones, hearing aids and robots, will be discussed. Our perspectives on the future of the field will be given as concluding remarks of this chapter
A compact noise covariance matrix model for MVDR beamforming
Acoustic beamforming is routinely used to improve the SNR of the received signal in applications such as hearing aids, robot audition, augmented reality, teleconferencing, source localisation and source tracking. The beamformer can be made adaptive by using an estimate of the time-varying noise covariance matrix in the spectral domain to determine an optimised beam pattern in each frequency bin that is specific to the acoustic environment and that can respond to temporal changes in it. However, robust estimation of the noise covariance matrix remains a challenging task especially in non-stationary acoustic environments. This paper presents a compact model of the signal covariance matrix that is defined by a small number of parameters whose values can be reliably estimated. The model leads to a robust estimate of the noise covariance matrix which can, in turn, be used to construct a beamformer. The performance of beamformers designed using this approach is evaluated for a spherical microphone array under a range of conditions using both simulated and measured room impulse responses. The proposed approach demonstrates consistent gains in intelligibility and perceptual quality metrics compared to the static and adaptive beamformers used as baselines
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