Abstract not found

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 statespace 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.

Abstract not found

Advances in microelectronics, array processing, and wireless networking, have motivated the analysis and design of low-cost integrated sensing, computating, and communicating nodes capable of performing various demanding collaborative space-time processing tasks. In this paper, we consider the problem of coherent acoustic sensor array processing and localization on distributed wireless sensor networks. We first introduce some basic concepts of beamforming and localization for wideband acoustic sources. A review of various known localization algorithms based on time-delay followed by LS estimations as well as maximum likelihood method is given. Issues related to practical implementation of coherent array processing including the need for fine-grain time synchronization are discussed. Then we describe the implementation of a Linux-based wireless networked acoustic sensor array testbed, utilizing commercially available iPAQs with built in microphones, codecs, and microprocessors, plus wireless Ethernet cards, to perform acoustic source localization. Various field-measured results using two localization algorithms show the effectiveness of the proposed testbed. An extensive list of references related to this work is also included.

Intelligent meeting rooms should support efficient and effective interactions among its occupants. In this paper, we present our efforts toward building intelligent environments using multimodal sensor network of static cameras, active (pan/tilt/zoom) cameras and microphone arrays. Active cameras are used to capture details associated with interesting events. The goal is not only to make the system that supports multiperson interactions in the environment in real time, but also to have the system remember the past, enabling review of past events in an intuitive and efficient manner. In this paper, we present the system specifications and major components, integration framework, active network control procedures and experimental studies involving multiperson interactions in an intelligent meeting room environment. 1.

One of the various human sensory capabilities is to identify the direction of perceived sounds. The goal of this work is to study sound source localization in three dimensions using some of the most important cues the human uses. Having robotics as a major application, the approach involves a compact sensor structure that can be placed on a mobile platform. The objective is to estimate the relative sound source position in three dimensional space without imposing excessive restrictions on its spatio-temporal characteristics and the environment structure. Two types of features are considered, interaural time and level differences. Their relative effectiveness for localization is studied, as well as a practical way of using these complementary parameters. A two-stage procedure was used. In the training stage, sound samples are produced from points with known coordinates and then are stored. In the recognition stage, unknown sounds are processed by the trained system t...

This paper proposes a solution to the problem of robust speaker localization under adverse acoustic conditions. The approach is based on the classification of time delay estimates. Two classification techniques are investigated in detail: maximum likelihood (ML) classification and classification based on histogram comparison. Their performance under adverse acoustic conditions is compared to outcomes obtained with the traditional approach which uses time delay estimates directly to infer speaker positions. Experiments indicate that the ML classification method provides little improvement over the traditional method. On the other hand, using histogram classification, we can improve the probability of correct speaker localization by more than 60 % compared to either the traditional approach or the ML classification technique. 1.

This paper presents an approach that enables a mobile "Interceptor" robot to intercept targets in an indoor environment using information from a distributed acoustic sensor network. The approach assumes the indoor environment has been previously mapped and that the sensor nodes know their position in the map. The targets are localized in the sensor network based upon local maxima of the acoustic volume. The current target localization information is reported to an Interceptor robot, which utilizes a dual wavefront path planner to move from its current location to a location that is within visibility range of a target. Results of the complete implementation of this approach using 70 sensor net robots in the Player/Stage multirobot simulator are reported, as well as implementation results to date on a team of physical robots. To our knowledge, this is the first implementation of a multi-robot system that combines the use of an acoustic sensor net for target detection with an Interceptor robot that can efficiently reach the moving position of the detected target in indoor environments.

Estimating the direction of arrival of sound in three dimensional space is typically performed by generalized time-delay processing on a set of signals from a fixed array of omnidirectional microphones. This requires specialized multichannel A/D hardware, and careful arrangement of the microphones into an array. This work is motivated by the desire to instead only use standard two-channel audio A/D hardware and portable equipment. To estimate direction of arrival of persistent sound, the position of the microphones is made variable by mounting them on one or more computer-controlled pan-and-tilt units. In this paper, we describe the signal processing and control algorithm of a device with two omnidirectional microphones on a fixed baseline and two rotational degrees of freedom. Experimental results with real data are reported with both impulsive and speech sounds in an untreated, normally reverberant indoor environment.

Abstract. We present an approach for tracking a lecturer during the course of his speech. We use features from multiple cameras and microphones, and process them in a joint particle filter framework. The filter performs sampled projections of 3D location hypotheses and scores them using features from both audio and video. On the video side, the features are based on foreground segmentation, multi-view face detection and upper body detection. On the audio side, the time delays of arrival between pairs of microphones are estimated with a generalized cross correlation function. In the CLEAR’06 evaluation, the system yielded a tracking accuracy (MOTA) of 71 % for video-only, 55 % for audio-only and 90 % for combined audio-visual tracking. 1