Microphone array

About: Microphone array is a research topic. Over the lifetime, 5936 publications have been published within this topic receiving 70026 citations.

Robust Sound Source Localization Using a Microphone Array on a Mobile Robot

Abstract: The hearing sense on a mobile robot is important because it is omnidirectional and it does not require direct line-of-sight with the sound source. Such capabilities can nicely complement vision to help localize a person or an interesting event in the environment. To do so the robot auditory system must be able to work in noisy, unknown and diverse environmental conditions. In this paper we present a robust sound source localization method in three-dimensional space using an array of 8 microphones. The method is based on time delay of arrival estimation. Results show that a mobile robot can localize in real time different types of sound sources over a range of 3 meters and with a precision of 3 degrees.

A steerable and variable first-order differential microphone array

Abstract: A new first-order differential microphone array with an infinitely steerable and variable beampattern is described. The microphone consists of 6 small pressure microphones flush-mounted on the surface of a 3/4" diameter rigid nylon sphere. The microphones are located on the surface at points where included octahedron vertices contact the spherical surface. By appropriately combining the three Cartesian orthogonal pairs with simple scalar weightings, a general first-order differential microphone beam (or beams) can be realized and directed to any angle in 4/spl pi/ steradian space. A working real-time version has been created and measured results from this microphone are shown. This microphone should be useful for surround sound recording/playback applications and to virtual reality audio applications.

A learning-based approach to direction of arrival estimation in noisy and reverberant environments

Abstract: This paper presents a learning-based approach to the task of direction of arrival estimation (DOA) from microphone array input. Traditional signal processing methods such as the classic least square (LS) method rely on strong assumptions on signal models and accurate estimations of time delay of arrival (TDOA) . They only work well in relatively clean conditions, but suffer from noise and reverberation distortions. In this paper, we propose a learning-based approach that can learn from a large amount of simulated noisy and reverberant microphone array inputs for robust DOA estimation. Specifically, we extract features from the generalised cross correlation (GCC) vectors and use a multilayer perceptron neural network to learn the nonlinear mapping from such features to the DOA. One advantage of the learning based method is that as more and more training data becomes available, the DOA estimation will become more and more accurate. Experimental results on simulated data show that the proposed learning based method produces much better results than the state-of-the-art LS method. The testing results on real data recorded in meeting rooms show improved root-mean-square error (RMSE) compared to the LS method.

Analysis of noise reduction and dereverberation techniques based on microphone arrays with postfiltering

Abstract: In teleconferencing systems, the use of hands-free sound pick-up reduces speech quality. This is due to ambient noise, acoustic echo, and the reverberation produced by the acoustical environment. This paper sets out to present a theoretical analysis of noise reduction and dereverberation algorithms based on a microphone array combined with a Wiener postfilter. It is shown that the transfer function of the postfilter depends on the input signal-to-noise ratio (SNR) and on the noise reduction yielded by the array. The use of a directivity-controlled array instead of a conventional beam-former is proposed to improve the performance of the whole system. Examples in real room environments are provided, which confirm the theoretical results, It is observed that the postfilter gives a limited reduction of the reverberation. On the contrary, an appreciable reduction of acoustic echo and localized noise is obtained and makes the whole system highly attractive for hands-free communication systems.

A Microphone Array System for Automatic Fall Detection

Abstract: More than a third of elderly fall each year in the United States. It has been shown that the longer the lie on the floor, the poorer is the outcome of the medical intervention. To reduce delay of the medical intervention, we have developed an acoustic fall detection system (acoustic-FADE) that automatically detects a fall and reports it promptly to the caregiver. Acoustic-FADE consists of a circular microphone array that captures the sounds in a room. When a sound is detected, acoustic-FADE locates the source, enhances the signal, and classifies it as “fall” or “nonfall.” The sound source is located using the steered response power with phase transform technique, which has been shown to be robust under noisy environments and resilient to reverberation effects. Signal enhancement is performed by the beamforming technique based on the estimated sound source location. Height information is used to increase the specificity. The mel-frequency cepstral coefficient features computed from the enhanced signal are utilized in the classification process. We have evaluated the performance of acoustic-FADE using simulated fall and nonfall sounds performed by three stunt actors trained to behave like elderly under different environmental conditions. Using a dataset consisting of 120 falls and 120 nonfalls, the acoustic-FADE achieves 100% sensitivity at a specificity of 97%.