Eric A. Lehmann

Particle filtering algorithms for tracking an acoustic source in a reverberant environment

Traditional acoustic source localization algorithms attempt to find the current location of the acoustic source using data collected at an array of sensors at the current time only. In the presence of strong multipath, these traditional algorithms often erroneously locate a multipath reflection rather than the true source location. A recently proposed approach that appears promising in overcoming this drawback of traditional algorithms, is a state-space approach using particle filtering. In this paper we formulate a general framework for tracking an acoustic source using particle filters. We discuss four specific algorithms that fit within this framework, and demonstrate their performance using both simulated reverberant data and data recorded in a moderately reverberant office room (with a measured reverberation time of 0.39 s). The results indicate that the proposed family of algorithms are able to accurately track a moving source in a moderately reverberant room.

Prediction of energy decay in room impulse responses simulated with an image-source model

A method is proposed that provides an approximation of the acoustic energy decay (energy-time curve) in room impulse responses generated using the image-source technique. A geometrical analysis of the image-source principle leads to a closed-form expression describing the energy decay curve, with the resulting formula being valid for a uniform as well as nonuniform definition of the enclosures six absorption coefficients. The accuracy of the proposed approximation is demonstrated on the basis of impulse-response simulations involving various room sizes and reverberation levels, with uniform and nonuniform sound absorption coefficients. An application example for the proposed method is illustrated by considering the task of predicting an enclosures reflection coefficients in order to achieve a specific reverberation level. The technique presented in this work enables designers to undertake a preliminary analysis of a simulated reverberant environment without the need for time-consuming image-method simulations.

Particle filter with integrated voice activity detection for acoustic source tracking

In noisy and reverberant environments, the problem of acoustic source localisation and tracking (ASLT) using an array of microphones presents a number of challenging difficulties. One of the main issues when considering real-world situations involving human speakers is the temporally discontinuous nature of speech signals: the presence of silence gaps in the speech can easily misguide the tracking algorithm, even in practical environments with low to moderate noise and reverberation levels. A natural extension of currently available sound source tracking algorithms is the integration of a voice activity detection (VAD) scheme. We describe a new ASLT algorithm based on a particle filtering (PF) approach, where VAD measurements are fused within the statistical framework of the PF implementation. Tracking accuracy results for the proposed method is presented on the basis of synthetic audio samples generated with the image method, whereas performance results obtained with a real-time implementation of the algorithm, and using real audio data recorded in a reverberant room, are published elsewhere. Compared to a previously proposed PF algorithm, the experimental results demonstrate the improved robustness of the method described in this work when tracking sources emitting real-world speech signals, which typically involve significant silence gaps between utterances.

Particle filter design using importance sampling for acoustic source localisation and tracking in reverberant environments

Sequential Monte Carlo methods have been recently proposed to deal with the problem of acoustic source localisation and tracking using an array of microphones. Previous implementations make use of the basic bootstrap particle filter, whereas a more general approach involves the concept of importance sampling. In this paper, we develop a new particle filter for acoustic source localisation using importance sampling, and compare its tracking ability with that of a bootstrap algorithm proposed previously in the literature. Experimental results obtained with simulated reverberant samples and real audio recordings demonstrate that the new algorithm is more suitable for practical applications due to its reinitialisation capabilities, despite showing a slightly lower average tracking accuracy. A real-time implementation of the algorithm also shows that the proposed particle filter can reliably track a person talking in real reverberant rooms.

Experimental comparison of particle filtering algorithms for acoustic source localization in a reverberant room

Traditional acoustic source localization techniques attempt to determine the current location of an acoustic source from data obtained at an array of sensors during the current time only. Recently, state-space methods have been proposed that use particle filters to perform recursive estimation of the current source location using all previous data. In this paper we present an overview of these particle filter algorithms, and formulate performance measures for determining their ability to track a moving source. We present results of experiments using reverberant data recorded in a real room, and show that steered beamforming methods have improved performance over GCC-based approaches.

Evolutionary Optimization of Dynamics Models in Sequential Monte Carlo Target Tracking

This paper describes a new method for the online parameter optimization of various models used to represent the target dynamics in particle filters. The optimization is performed with an evolutionary strategy algorithm, by using the performance of the particle filter as a basis for the objective function. Two different approaches to forming the objective function are presented: the first assumes knowledge of the true source position during the optimization, and the second uses the position estimates from the particle filter to form an estimate of the current ground-truth data. The new algorithm has low computational complexity and is suitable for real-time implementation. A simple and intuitive real-world application of acoustic source localization and tracking is used to highlight the performance of the algorithm. Results show that the algorithm converges to an optimum tracker for any type of dynamics model that is capable of representing the target dynamics.

Particle Filtering Approach to Adaptive Time-Delay Estimation

A particle filter algorithm is developed for the problem of online subsample time-delay estimation between noisy signals received at two spatially separated sensors. The delay is modeled as an adaptive FIR filter whose coefficients are determined by the trackers particles, and updated on a sample-by-sample basis. Efficient tracking of the delay parameter over time is ensured with the derivation of a global system model integrating the target dynamics for both near-field and far-field operation. Experimental simulations are carried out to assess the algorithms convergence and tracking performance, and demonstrate that the proposed method is able to efficiently track time delays with stationary signals as well as speech

Modeling of Motion Dynamics and its Influence on the Performance of a Particle Filter for Acoustic Speaker Tracking

Methods for acoustic speaker tracking attempt to localize and track the position of a sound source in a reverberant environment using the data received at an array of microphones. This problem has received significant attention over the last few years, with methods based on a particle filtering principle perhaps representing one of the most promising approaches. As a Bayesian filtering technique, a particle filter relies on the definition of two main concepts, namely the measurement process and the transition equation (target dynamics). Whereas a significant research effort has been devoted to the development of improved measurement processes, the influence of the dynamics formulation on the resulting tracking accuracy has received little attention so far. This paper provides an insight into the dynamics modeling aspect of particle filter design. Several types of motion models are considered, and the performance of the resulting particle filters is then assessed with extensive experimental simulations using real audio data recorded in a reverberant environment. This paper demonstrates that the ability to achieve a reduced tracking error relies on both the chosen model as well as the specific optimization of its parameters.