Daniele Salvati

Adaptive Time Delay Estimation Using Filter Length Constraints for Source Localization in Reverberant Acoustic Environments

Adaptive time delay estimation based on blind system identification (BSI) focuses on the impulse responses between a source and a microphone to estimate the time difference of arrival (TDOA) in reverberant environments. In this letter, we consider the adaptive eigenvalue decomposition (AED) BSI method based on the normalized multichannel frequency-domain least mean square (NMCFLMS) algorithm. We show that the use of filter length constraints (FLC) based on the maximum TDOA between microphones improves the performance of the NMCFLMS filter for the localization of different sound types in highly reverberant environments. The experimental results demonstrate the improvement of the proposed method for reverberation times (RT60) of up to 2 s. Applications for this method include teleconferencing systems, musical interfaces, videogames, and monitoring systems.

Incoherent Frequency Fusion for Broadband Steered Response Power Algorithms in Noisy Environments

The steered response power (SRP) algorithms have been shown to be among the most effective and robust ones in noisy environments for direction of arrival (DOA) estimation. In broadband signal applications, the SRP methods typically perform their computations in the frequency-domain by applying a fast Fourier transform (FFT) on a signal portion, calculating the response power on each frequency bin, and subsequently fusing these estimates to obtain the final result. We introduce a frequency response incoherent fusion method based on a normalized arithmetic mean (NAM). Experiments are presented that rely on the SRP algorithms for the localization of motor vehicles in a noisy outdoor environment, focusing our discussion on performance differences with respect to different signal-to-noise ratios (SNR), and on spatial resolution issues for closely spaced sources. We demonstrate that the proposed fusion method provides higher resolution for the delay-and-sum SRP, and improved performances for minimum variance distortionless response (MVDR) and multiple signal classification (MUSIC).

Incident Signal Power Comparison for Localization of Concurrent Multiple Acoustic Sources

In this paper, a method to solve the localization of concurrent multiple acoustic sources in large open spaces is presented. The problem of the multisource localization in far-field conditions is to correctly associate the direction of arrival (DOA) estimated by a network array system to the same source. The use of systems implementing a Bayesian filter is a traditional approach to address the problem of localization in multisource acoustic scenario. However, in a real noisy open space the acoustic sources are often discontinuous with numerous short-duration events and thus the filtering methods may have difficulty to track the multiple sources. Incident signal power comparison (ISPC) is proposed to compute DOAs association. ISPC is based on identifying the incident signal power (ISP) of the sources on a microphone array using beamforming methods and comparing the ISP between different arrays using spectral distance (SD) measurement techniques. This method solves the ambiguities, due to the presence of simultaneous sources, by identifying sounds through a minimization of an error criterion on SD measures of DOA combinations. The experimental results were conducted in an outdoor real noisy environment and the ISPC performance is reported using different beamforming techniques and SD functions.

A weighted MVDR beamformer based on SVM learning for sound source localization

A WMVDR beamformer for sound source localization in a reverberant room is proposed.The weighted coefficients are modeled by a SVM classifier.The skewness measure of marginal distributions is proposed as input feature.Classify the narrowband power maps into constructively and disruptively contributing. A weighted minimum variance distortionless response (WMVDR) algorithm for near-field sound localization in a reverberant environment is presented. The steered response power computation of the WMVDR is based on a machine learning component which improves the incoherent frequency fusion of the narrowband power maps. A support vector machine (SVM) classifier is adopted to select the components of the fusion. The skewness measure of the narrowband power map marginal distribution is showed to be an effective feature for the supervised learning of the power map selection. Experiments with both simulated and real data demonstrate the improvement of the WMVDR beamformer localization accuracy with respect to other state-of-the-art techniques.

On the use of machine learning in microphone array beamforming for far-field sound source localization

This paper presents a weighted minimum variance distortionless response (WMVDR) algorithm for far-field sound source localization in a noisy environment. The broadband beam-forming is computed in the frequency-domain by calculating the response power on each frequency bin and by fusing the narrowband components. A machine learning method based on a support vector machine (SVM) is used for selecting only the narrowband components that positively contribute to the broadband fusion. We investigate the direction of arrival (DOA) estimation problem using a uniform linear array (ULA). The skewness measure of response power function is used as input feature for the supervised SVM learning. Simulations demonstrate the effectiveness of the WMVDR in an outdoor noisy environment.

Sensitivity-based region selection in the steered response power algorithm

Abstract The steered response power (SRP) algorithm is a well-studied method for acoustic source localization using a microphone array. Recently, different improvements based on the accumulation of all time difference of arrival (TDOA) information have been proposed in order to achieve spatial resolution scalability of the grid search map and reduce the computational cost. However, the TDOA information distribution is not uniform with respect to the search grid, as it depends on the geometry of the array, the sampling frequency, and the spatial resolution. In this paper, we propose a sensitivity-based region selection SRP (R-SRP) algorithm that exploits the nonuniform TDOA information accumulation on the search grid. First, high and low sensitivity regions of the search space are identified using an array sensitivity estimation procedure; then, through the formulation of a peak-to-peak ratio (PPR) measuring the peak energy distribution in the two regions, the source is classified to belong to a high or to a low sensitivity region, and this information is used to design an ad hoc weighting function of the acoustic power map on which the grid search is performed. Simulated and real experiments show that the proposed method improves the localization performance in comparison to the state-of-the-art.

Exploiting a Geometrically Sampled Grid in the SRP-PHAT for Localization Improvement and Power Response Sensitivity Analysis

The steered response power phase transform (SRP- PHAT) is a beamformer method very attractive in acoustic local- ization applications due to its robustness in reverberant environ- ments. This paper presents a spatial grid design procedure, called the geometrically sampled grid (GSG), which aims at computing the spatial grid by taking into account the discrete sampling of time difference of arrival (TDOA) functions and the desired spatial resolution. A new SRP-PHAT localization algorithm based on the GSG method is also introduced. The proposed method exploits the intersections of the discrete hyperboloids representing the TDOA information domain of the sensor array, and projects the whole TDOA information on the space search grid. The GSG method thus allows to design the sampled spatial grid which represents the best search grid for a given sensor array, it allows to perform a sensitivity analysis of the array and to characterize its spatial localization accuracy, and it may assist the system designer in the reconfiguration of the array. Experimental results using both simulated data and real recordings show that the localization accuracy is substantially improved both for high and for low spatial resolution, and that it is closely related to the proposed power response sensitivity measure. Index Terms—Sound source localization, steered response power, acoustic beamforming, SRP-PHAT, geometrically sampled grid, power response sensitivity analysis, microphone array, reverberant environment.

Sound Source and Microphone Localization From Acoustic Impulse Responses

This letter proposes a new method for source and microphone localization in reverberant environments using a randomly arranged sensor array, under the hypothesis that the position of one reference sensor and the geometry of the environment are known, and the other microphone positions are unknown. A minimum mean square error (MMSE) estimator that exploits early reflections is proposed. The MMSE estimator is solved by a grid search method that combines the information on early reflections estimated using a multichannel blind system identification and the time difference of arrivals between the reflections and the direct-path calculated with the image-source model. Simulations under different reverberant scenarios demonstrate the ability of the proposed approaches in localizing source and microphone.

Multiple acoustic sources localization using incident Signal Power comparison

We present a novel approach to locate multiple acoustic sources in far-field environments, in order to solve an interesting problem in different application domain, such as: audio surveillance systems and soundscape analysis frameworks. This approach aims at finding a solution to the ambiguities in Direction Of Arrivals (DOAs) combination caused by simultaneous multiple sources. The algorithm is based on two steps: the separation of the sources by means of beamforming techniques and the comparison of the Incident Signal Power (ISP) spectrum by means of a spectral distance measure. We implemented a prototype, composed by two linear arrays, that has been successfully tested in a real noisy environment.

Diagonal Unloading Beamforming for Source Localization.

In acoustic array processing, beamforming is a class of algorithms commonly used to estimate the position of a radiating sound source. This paper presents a diagonal unloading (DU) transformation method for the conventional response power beamforming to achieve robust localization with low computational complexity. The transformation is obtained by subtracting an opportune diagonal matrix from the covariance matrix of the array output vector. Specifically, the DU beamformer aims at subtracting the signal subspace from the noisy signal space. It is, hence, a data-dependent covariance matrix conditioning method. We show how to calculate precisely the unloading parameters, and we present a comparison of the proposed DU beamforming, the robust minimum variance distortionless response (MVDR) filter, and the multiple signal classification (MUSIC) method, in terms of their respective eigenanalyses. Theoretical analysis and experiments conducted on both simulated and real acoustic data demonstrate that the DU beamformer localization performance is comparable to that of robust MVDR and MUSIC. Since its computational cost is equivalent to that of a conventional beamformer, the proposed DU beamformer method can, thus, be very attractive due to its effectiveness and computational efficiency.