Sound Capture and Processing

Tashev, Ivan

doi:10.1002/9780470994443

Cited by 111 publications

(108 citation statements)

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“…МР нашли применение в решении широкого круга задач, прежде всего благодаря многообразию алгоритмов обработки. Выбор алгоритмов обработки обусловлен во мно-гом сценариями применения МР, к которым можно отнести следующие:  обработка в реальном масштабе времени (on line)/пост обработка (off line);  сценарии близкого/удаленного диктора;  сценарии стационарной/динамической (меняющейся) акустической обстановки Для разных сценариев предложено большое число разных алгоритмов обработки сигналов, кото-рым посвящена обширная литература (например, [3][4][5][6][7][8][9][10][11]). В рамках настоящего обзора не представляется возможным описать данные алгоритмы сколько-нибудь подробно.…”

Section: алгоритмы обработки сигналов микрофонных решетокunclassified

“…В настоящее время тематика МР представ-ляет собой обширное направление в области цифровой обработки сигналов. Детальное описание прин-ципов конструирования и алгоритмов обработки сигналов МР представлено в ряде фундаментальных работ [3][4][5][6][7][8][9][10][11].…”

Section: Introductionunclassified

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Application of microphone arrays for distant speech capture

Борисович¹

2015

Naučno-teh. vestn. inf. tehnol. meh. opt.

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Section: алгоритмы обработки сигналов микрофонных решетокunclassified

Section: Introductionunclassified

Application of microphone arrays for distant speech capture

Борисович¹

2015

Naučno-teh. vestn. inf. tehnol. meh. opt.

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“…In (1), ⌦c = (✓c, c) indexes the elevation and the azimuth of the look-up point, D⌦ c (f ) is the vector of transfer functions from the look-up point to the microphones, and NN (f ) is the noise cross-power spectrum, typically diagonally loaded with the microphone self-noise and the representation of the manufacturing tolerances (Chapter 5, [11]). We assume the far-field regime, so that the look-up points lie on sufficiently large sphere.…”

Section: Array Geometries and Beamformingmentioning

confidence: 99%

“…This is equivalent to the output of a single microphone with the directivity pattern B(f, ⌦) = W ⇤ ⌦c (f )D⌦(f ), located at the center of the array. We define the directivity index for the direction ⌦T as [11] …”

Section: Array Geometries and Beamformingmentioning

confidence: 99%

Hardware and algorithms for ultrasonic depth imaging

Tashev¹

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Depth imaging is commonly based on light. For example, LIDAR and Kinect use infrared light, while stereo cameras use visible light. These systems require hardware operating at high sampling frequencies, precise calibration, and they dissipate significant power. In this paper, we investigate the potential of ultrasound for image and depth acquisition, with applications to human-computer interaction and skeletal tracking in mind. We use a loudspeaker array and a microphone array to sense the scene. We discuss a technique for offline loudspeaker beamforming (commonly used for microphone beamforming) which enables us to significantly increase the frame rate. Further, we propose a sound-source-localization-based method for computing the depth image, giving a substantial improvement over the naïve time-of-flight approach. We designed inexpensive hardware with eight elements per array to obtain both the depth and the intensity images. Even with this limited number of transducers we obtain promising experimental results.

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“…Speech signals captured with distant microphones inevitably contain interfering noise and reverberation, *Correspondence: kinoshita.k@lab.ntt.co.jp 1 NTT Communication Science Laboratories, Kyoto, Japan Full list of author information is available at the end of the article which severely degrade the audible speech quality of the captured signals [1] and the performance of automatic speech recognition (ASR) [2,3]. A reverberant speech signal y(t) at time t can be expressed as…”

Section: Introductionmentioning

confidence: 99%

A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research

Kinoshita

Delcroix

Gannot

et al. 2016

EURASIP J. Adv. Signal Process.

306

225

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In recent years, substantial progress has been made in the field of reverberant speech signal processing, including both single-and multichannel dereverberation techniques and automatic speech recognition (ASR) techniques that are robust to reverberation. In this paper, we describe the REVERB challenge, which is an evaluation campaign that was designed to evaluate such speech enhancement (SE) and ASR techniques to reveal the state-of-the-art techniques and obtain new insights regarding potential future research directions. Even though most existing benchmark tasks and challenges for distant speech processing focus on the noise robustness issue and sometimes only on a single-channel scenario, a particular novelty of the REVERB challenge is that it is carefully designed to test robustness against reverberation, based on both real, single-channel, and multichannel recordings. This challenge attracted 27 papers, which represent 25 systems specifically designed for SE purposes and 49 systems specifically designed for ASR purposes. This paper describes the problems dealt within the challenge, provides an overview of the submitted systems, and scrutinizes them to clarify what current processing strategies appear effective in reverberant speech processing.

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Sound Capture and Processing

Cited by 111 publications

References 0 publications

Application of microphone arrays for distant speech capture

Application of microphone arrays for distant speech capture

Hardware and algorithms for ultrasonic depth imaging

A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research

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