Anthony Lombard

TDOA Estimation for Multiple Sound Sources in Noisy and Reverberant Environments Using Broadband Independent Component Analysis

In this paper, we show that minimization of the statistical dependence using broadband independent component analysis (ICA) can be successfully exploited for acoustic source localization. As the ICA signal model inherently accounts for the presence of several sources and multiple sound propagation paths, the ICA criterion offers a theoretically more rigorous framework than conventional techniques based on an idealized single-path and single-source signal model. This leads to algorithms which outperform other localization methods, especially in the presence of multiple simultaneously active sound sources and under adverse conditions, notably in reverberant environments. Three methods are investigated to extract the time difference of arrival (TDOA) information contained in the filters of a two-channel broadband ICA scheme. While for the first, the blind system identification (BSI) approach, the number of sources should be restricted to the number of sensors, the other methods, the averaged directivity pattern (ADP) and composite mapped filter (CMF) approaches can be used even when the number of sources exceeds the number of sensors. To allow fast tracking of moving sources, the ICA algorithm operates in block-wise batch mode, with a proportionate weighting of the natural gradient to speed up the convergence of the algorithm. The TDOA estimation accuracy of the proposed schemes is assessed in highly noisy and reverberant environments for two, three, and four stationary noise sources with speech-weighted spectral envelopes as well as for moving real speech sources.

Multidimensional Localization of Multiple Sound Sources Using Blind Adaptive MIMO System Identification

The TDOA-based acoustic source localization approach is a powerful and widely-used method which can be applied for one source in several dimensions or several sources in one dimension. However the localization turns out to be more challenging when multiple sound sources should be localized in multiple dimensions, due to a spatial ambiguity phenomenon which requires to perform an intermediate step after the TDOA estimation and before the calculation of the geometrical source positions. In order to obtain the required set of TDOA estimates for the multidimensional localization of multiple sound sources, we apply a recently presented TDOA estimation method based on blind adaptive multiple-input-multiple-output (MIMO) system identification. We demonstrate that this localization method also provides valuable side information which allows us to resolve the spatial ambiguity without any prior knowledge about the source positions. Furthermore we show that the blind adaptive MIMO system identification allows a high spatial resolution. Experimental results for the localization of two sources in a two-dimensional plane show the effectiveness of the proposed scheme

Multidimensional localization of multiple sound sources using averaged directivity patterns of Blind Source Separation systems

In this paper, we propose a versatile acoustic source localization framework exploiting the self-steering capability of Blind Source Separation (BSS) algorithms. We provide a way to produce an acoustical map of the scene by computing the averaged directivity pattern of BSS demixing systems. Since BSS explicitly accounts for multiple sources in its signal propagation model, several simultaneously active sound sources can be located using this method. Moreover, the framework is suitable to any microphone array geometry, which allows application for multiple dimensions, in the near field as well as in the far field. Experiments demonstrate the efficiency of the proposed scheme in a reverberant environment for the localization of speech sources.

Combination of adaptive feedback cancellation and binaural adaptive filtering in hearing aids

We study a system combining adaptive feedback cancellation and adaptive filtering connecting inputs from both ears for signal enhancement in hearing aids. For the first time, such a binaural system is analyzed in terms of system stability, convergence of the algorithms, and possible interaction effects. As major outcomes of this study, a new stability condition adapted to the considered binaural scenario is presented, some already existing and commonly used feedback cancellation performance measures for the unilateral case are adapted to the binaural case, and possible interaction effects between the algorithms are identified. For illustration purposes, a blind source separation algorithm has been chosen as an example for adaptive binaural spatial filtering. Experimental results for binaural hearing aids confirm the theoretical findings and the validity of the new measures.

Disambiguation in multidimensional tracking of multiple acoustic sources using a Gaussian likelihood criterion

The TDOA-based acoustic source localization approaches suffer from a pairing problem in the case of multiple dimensions and multiple sources. To resolve this problem, usually three or more microphone pairs are required. In this paper, we propose a new solution based on a Gaussian likelihood function to pair TDOAs, which allows us to localize multiple moving acoustic sources in several dimensions using a minimum number of microphone pairs. Moreover, the proposed method is combined with a particle filter for tracking multiple moving sources. Experimental results demonstrate the effectiveness of the proposed scheme.

An improved combination of Directional BSS and a source localizer for robust source separation in rapidly time-varying acoustic scenarios

In rapidly time-varying acoustic scenarios, Blind Source Separation (BSS) often suffers from problems with slow convergence and poor separation performance. In this paper, we propose an improved scheme composed of Directional BSS and a source localizer for robustly separating and quickly tracking sources in a rapidly time-varying scene. Directional BSS is defined as a generic BSS algorithm combined with geometric constraints which are roughly equivalent to a set of delay&subtract beamformers. This scheme combines benefits from BSS and null beamforming and achieves high performance for separating sources of known locations. For estimating the Time-Difference-Of-Arrival (TDOA) of each source, another BSS system running in parallel to the main Directional BSS is exploited to serve as a source localizer. Experimental results illustrate the efficiency of the proposed concept.

An acoustic front-end for interactive TV incorporating multichannel acoustic echo cancellation and blind signal extraction

In this contribution, an acoustic front-end for distant-talking interfaces as developed within the European Union-funded project DICIT (Distant-talking interfaces for Control of Interactive TV) is presented. It comprises state-of-the-art multichannel acoustic echo cancellation and blind source separation-based signal extraction and only requires two microphone signals. The proposed scheme is analyzed and evaluated for different realistic scenarios when a speech recognizer is used as back-end. The results show that the system significantly outperforms simple alternatives, i.e., a two-channel Delay & Sum beamformer for speech signal extraction.

Frequency-domain Comfort Noise Generation for Discontinuous Transmission in EVS

Discontinuous Transmission (DTX) is an efficient way to drastically reduce the transmission rate of a communication codec in the absence of voice input. In this mode, most frames that are determined to consist of background noise only are dropped from transmission and replaced by some Comfort Noise Generation (CNG) in the decoder. In this paper, we propose a novel CNG approach combining information gained about the actual background noise at both encoder and decoder side. It is able to better reproduce background noise types showing a pronounced spectral tilt, which is difficult for traditional schemes based on a linear prediction model. The proposed technique operates in the frequency domain. It is part of the Enhanced Voice Services (EVS) codec, where it is known as FD-CNG. Listening tests show the superior quality of FD-CNG over existing approaches for certain background noise such as car noise.

A Real-Time Demonstrator for the 2D Localization of two Sound Sources Using Blind Adaptive MIMO System Identification

A real-time demonstrator for the 2D localization of two sound sources using two microphone pairs is presented and evaluated. The scheme relies on Blind Source Separation (BSS) to adaptively identify the acoustical MIMO system, hence allowing the estimation of relative time delays for each source and each dimension. Extending our previously presented work [1], a mechanism to solve a pairing problem occuring in the multidimensional localization of several sources is described. It exploits the inherent signal extraction abilities of BSS. Experimental evaluations with large microphone apertures show that the demonstrator can accurately localize two speech sources in a 2D space, with a precision better than one degree.

'Shadow BSS' for blind source separation in rapidly time-varying acoustic scenes

This paper addresses the tracking capability of blind source separation algorithms for rapidly time-varying sensor or source positions. Based on a known algorithm for blind source separation, which also allows for simultaneous localization of multiple active sources in reverberant environments, the source separation performance will be investigated for abrupt microphone array rotations representing the worst case. After illustrating the deficiencies in source-tracking with the given efficient implementation of the BSS algorithm, a method to ensure robust source separation even with abrupt microphone array rotations is proposed. Experimental results illustrate the efficiency of the proposed concept.