876 research outputs found
SoundCompass: a distributed MEMS microphone array-based sensor for sound source localization
Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field
FPGA-based architectures for acoustic beamforming with microphone arrays : trends, challenges and research opportunities
Over the past decades, many systems composed of arrays of microphones have been developed to satisfy the quality demanded by acoustic applications. Such microphone arrays are sound acquisition systems composed of multiple microphones used to sample the sound field with spatial diversity. The relatively recent adoption of Field-Programmable Gate Arrays (FPGAs) to manage the audio data samples and to perform the signal processing operations such as filtering or beamforming has lead to customizable architectures able to satisfy the most demanding computational, power or performance acoustic applications. The presented work provides an overview of the current FPGA-based architectures and how FPGAs are exploited for different acoustic applications. Current trends on the use of this technology, pending challenges and open research opportunities on the use of FPGAs for acoustic applications using microphone arrays are presented and discussed
Development of Novel Independent Component Analysis Techniques and their Applications
Real world problems very often provide minimum information regarding their causes. This is mainly due to the system complexities and noninvasive techniques employed by scientists and engineers to study such systems. Signal and image processing techniques used for analyzing such systems essentially tend to be blind. Earlier, training signal based techniques were used extensively for such analyses. But many times either these training signals are not practicable to be availed by the analyzer or become burden on the system itself. Hence blind signal/image processing techniques are becoming predominant in modern real time systems. In fact, blind signal processing has become a very important topic of research and development in many areas, especially biomedical engineering, medical imaging, speech enhancement, remote sensing, communication systems, exploration seismology, geophysics, econometrics, data mining, sensor networks etc. Blind Signal Processing has three major areas: Blind Signal Separation and Extraction, Independent Component Analysis (ICA) and Multichannel Blind Deconvolution and Equalization. ICA technique has also been typically applied to the other two areas mentioned above. Hence ICA research with its wide range of applications is quite interesting and has been taken up as the central domain of the present work
FPGA Implementation of Blind Source Separation using FastICA
Fast Independent Component Analysis (FastICA) is a statistical method used to separate signals from an unknown mixture without any prior knowledge about the signals. This method has been used in many applications like the separation of fetal and maternal Electrocardiogram (ECG) for pregnant women. This thesis presents an implementation of a fixed-point FastICA in field programmable gate array (FPGA). The proposed design can separate up to four signals using four sensors. QR decomposition is used to improve the speed of evaluation of the eigenvalues and eigenvectors of the covariance matrix. Moreover, a symmetric orthogonalization of the unit estimation algorithm is implemented using an iterative technique to speed up the search algorithm for higher order data input. The hardware is implemented using Xilinx virtex5-XC5VLX50t chip. The proposed design can process 128 samples for the four sensors in less than 63 ns when the design is simulated using 10 MHz clock
LibriWASN: A Data Set for Meeting Separation, Diarization, and Recognition with Asynchronous Recording Devices
We present LibriWASN, a data set whose design follows closely the LibriCSS
meeting recognition data set, with the marked difference that the data is
recorded with devices that are randomly positioned on a meeting table and whose
sampling clocks are not synchronized. Nine different devices, five smartphones
with a single recording channel and four microphone arrays, are used to record
a total of 29 channels. Other than that, the data set follows closely the
LibriCSS design: the same LibriSpeech sentences are played back from eight
loudspeakers arranged around a meeting table and the data is organized in
subsets with different percentages of speech overlap. LibriWASN is meant as a
test set for clock synchronization algorithms, meeting separation, diarization
and transcription systems on ad-hoc wireless acoustic sensor networks. Due to
its similarity to LibriCSS, meeting transcription systems developed for the
former can readily be tested on LibriWASN. The data set is recorded in two
different rooms and is complemented with ground-truth diarization information
of who speaks when.Comment: Accepted for presentation at the ITG conference on Speech
Communication 202
Detection and Processing Techniques of FECG Signal for Fetal Monitoring
Fetal electrocardiogram (FECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies are becoming very important requirements in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and developed algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature for fetal monitoring. A comparative study has been carried out to show the performance and accuracy of various methods of FECG signal analysis for fetal monitoring. Finally, this paper further focused some of the hardware implementations using electrical signals for monitoring the fetal heart rate. This paper opens up a passage for researchers, physicians, and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system
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