Microphone array network for ubiquitous sound acquisition

Takagi, Tomoya; Noguchi, Hiroki; Kugata, Koji; Yoshimoto, Masahiko; Kawaguchi, Hiroshi

doi:10.1109/icassp.2010.5495490

Cited by 10 publications

(8 citation statements)

References 4 publications

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“…The LOUD project [21] developed a 1020-microphone array to achieve substantial noise/interference suppression. Adaptive noise suppression and audio beamforming algorithms are studied in [25,26] and [27] provides an informative survey. Such audio-enhancement algorithms have been applied to in acoustic sensor networks [28] that form a microphone array.…”

Section: Related Workmentioning

confidence: 99%

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Autodirective audio capturing through a synchronized smartphone array

Sur

Wei

Zhang

2014

Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services

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High-quality, speaker-location-aware audio capturing has traditionally been realized using dedicated microphone arrays. But high cost and lack of portability prevents such systems from being widely adopted. Today's smartphones are relatively more convenient for audio recording, but the audio quality is much lower in noisy environment and speaker location cannot be readily obtained. In this paper, we design and implement Dia, which leverages smartphone cooperation to overcome the above limitations. Dia supports spontaneous setup, by allowing a group of users to rapidly assemble an array of smartphones to emulate a dedicated microphone array. It employs a novel framework to accurately synchronize the audio I/O clocks of the smartphones. The synchronized smartphone array further enables autodirective audio capturing, i.e., tracking the speaker's location, and beamforming the audio capturing towards the speaker to improve audio quality. We implement Dia on a testbed consisting of 8 Android phones. Our experiments demonstrate that Dia can synchronize the microphones of different smartphones with sample-level accuracy. It achieves high localization accuracy, and similar beamforming performance compared with a microphone array with perfect synchronization.

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Section: Related Workmentioning

confidence: 99%

“…More powerful microphone array with a large number of elements have been developed. In [25], a 16-microphone array is placed in an office environment for speech enhancement. The LOUD project [21] developed a 1020-microphone array to achieve substantial noise/interference suppression.…”

Section: Related Workmentioning

confidence: 99%

Autodirective audio capturing through a synchronized smartphone array

Sur

Wei

Zhang

2014

Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services

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“…To implement a microphone array as a real ubiquitous sound acquisition system, we have proposed division of the huge array into sub-arrays to produce a multi-hop network: an intelligent ubiquitous sensor network (IUSN) [6][7][8]. The sub-array nodes can be set up on the walls and ceiling of a room.…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, a basic sound-source localization algorithm with a high degree of accuracy is important to produce a perfect aggregation scheme. To achieve highly accurate sound source localization, we have already proposed a hierarchical sound-source localization method [7] based on the multiple signal classification (MUSIC) algorithm [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Low-Traffic and Low-Power Data-Intensive Sound Acquisition with Perfect Aggregation Specialized for Microphone Array Networks

Noguchi

Takagi

Kugata

et al. 2010

2010 Fourth International Conference on Sensor Technologies and Applications

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We propose a microphone array network that realizes ubiquitous sound acquisition. Several nodes with 16 microphones are connected to form a novel huge sound acquisition system, which carries out voice activity detection (VAD), sound source localization, and separation. The three operations are distributed among nodes. Using the distributed network, we achieve a low-traffic data-intensive array network. To manage nodes' power consumption, VAD is implemented. Consequently, the system uses little power when speech is not active. For sound localization, a networkconnected multiple signal classification (MUSIC) algorithm is used. The sound separation system can improve a signal-noise ratio (SNR) by 7.75 dB using 112 microphones. Network traffic is reduced by 99.11% when using 1024 microphones.

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3D sound field analysis using circular higher-order microphone array

Chen

Abhayapala

Zhang

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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This paper proposes the theory and design of circular higher-order microphone arrays for 3D sound field analysis using spherical harmonics. Through employing the spherical harmonic translation theorem, the local spatial sound fields recorded by each higher-order microphone placed in the circular arrays are combined to form the sound field information of a large global spherical region. The proposed design reduces the number of the required sampling points and the geometrical complexity of microphone arrays. We develop a two-step method to calculate sound field coefficients using the proposed array structure, i) analytically combine local sound field coefficients on each circular array and ii) solve for global sound field coefficients using data from the first step. Simulation and experimental results show that the proposed array is capable of acquiring the full 3D sound field information over a relatively large spherical region with decent accuracy and computational simplicity.

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Microphone array network for ubiquitous sound acquisition

Cited by 10 publications

References 4 publications

Autodirective audio capturing through a synchronized smartphone array

Autodirective audio capturing through a synchronized smartphone array

Low-Traffic and Low-Power Data-Intensive Sound Acquisition with Perfect Aggregation Specialized for Microphone Array Networks

3D sound field analysis using circular higher-order microphone array

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