传输分集 beamforming_多天线增益_CiteSeerX Citation Query Beamforming:(2)

... [16] and falls within the general category of linearly constrained minimum variance (LCMV) filtering. Spatial filtering or “beamforming” using LCMV methods has been widely applied in radar and sonar =-=[17]-=-. Early work on LCMV beamforming includes [18] and [19]. The map of neural power would appear to provide a useful metric for source localization by associating sources with regions of large neural pow...

On ideal binary mask as the computational goal of auditory scene analysis

by Deliang Wang - in Speech Separation by Humans and Machines , 2005

"... In a natural environment, a target sound, such as speech, is usually mixed with acoustic interference. A sound separation system that removes or attenuates acoustic interference has many important applications, such as automatic speech recognition (ASR) and speaker identification in real ..."

Abstract - Cited by 87 (39 self) - Add to MetaCart

In a natural environment, a target sound, such as speech, is usually mixed with acoustic interference. A sound separation system that removes or attenuates acoustic interference has many important applications, such as automatic speech recognition (ASR) and speaker identification in real

Signal Enhancement Using beamforming and Non-Stationarity with Applications to Speech

by Sharon Gannot, Student Member, David Burshtein, Senior Member, Ehud Weinstein - IEEE Trans. Signal Processing

"... Abstract—We consider a sensor array located in an enclo-sure, where arbitrary transfer functions (TFs) relate the source signal and the sensors. The array is used for enhancing a signal contaminated by interference. Constrained minimum power adaptive beamforming, which has been suggested by Frost an ..."

Abstract - Cited by 68 (9 self) - Add to MetaCart

Abstract—We consider a sensor array located in an enclo-sure, where arbitrary transfer functions (TFs) relate the source signal and the sensors. The array is used for enhancing a signal contaminated by interference. Constrained minimum power adaptive beamforming, which has been suggested by Frost and, in particular, the generalized sidelobe canceler (GSC) version, which has been developed by Griffiths and Jim, are the most widely used beamforming techniques. These methods rely on the assumption that the received signals are simple delayed versions of the source signal. The good interference suppression attained under this assumption is severely impaired in complicated acoustic environments, where arbitrary TFs may be encountered. In this paper, we consider the arbitrary TF case. We propose a GSC solution, which is adapted to the general TF case. We derive a suboptimal algorithm that can be implemented by estimating the TFs ratios, instead of estimating the TFs. The TF ratios are estimated by exploiting the nonstationarity characteristics of the desired signal. The algorithm is applied to the problem of speech enhancement in a reverberating room. The discussion is supported by an experimental study using speech and noise signals recorded in an actual room acoustics environment. Index Terms—beamforming, nonstationarity, speech enhance-ment. I.

Coherent Acoustic Array Processing and Localization on Wireless Sensor Networks

by J.C. Chen, L. Yip, J. Elson, H. Wang, D. Maniezzo, Student Member, R. E. Hudson, K. Yao, D. Estrin - Proc. the IEEE , 2003

"... 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 probl ..."

Abstract - Cited by 49 (4 self) - Add to MetaCart

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.

Citation Context

本文来自电脑杂谈，转载请注明本文网址：
http://www.pc-fly.com/a/jisuanjixue/article-7801-2.html