A digital microphone array for distant speech recognition

Zwyssig, Erich; Lincoln, Mike; Renals, Steve

doi:10.1109/icassp.2010.5495040

Cited by 22 publications

(23 citation statements)

References 21 publications

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“…Indoor applications, such as videoconferencing, home surveillance, and patient care, make also use of microphone arrays for speech detection [1,7]. This paper describes the design and implementation on an FPGA of an eightelement digital MEMS microphone array for distant speech recognition.…”

Section: Related Workmentioning

confidence: 99%

“…Current FPGAs are a potential solution thanks to their high-computational power and low latency response. In fact, FPGAs have been already considered by other researchers, mainly for converting the analogue or digital microphone signals into an audio format [1,2] without further signal processing computation. We believe that FPGAs not only are able to manage relatively large microphone arrays, but also enable a faster response when compared to using general purpose processors.…”

Section: Introductionmentioning

confidence: 99%

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Design Considerations When Accelerating an FPGA-Based Digital Microphone Array for Sound-Source Localization

et al. 2017

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The use of microphone arrays for sound-source localization is a well-researched topic. The response of such sensor arrays is dependent on the quantity of microphones operating on the array. A higher number of microphones, however, increase the computational demand, making real-time response challenging. In this paper, we present a Filter-and-Sum based architecture and several acceleration techniques to provide accurate sound-source localization in real-time. Experiments demonstrate how an accurate sound-source localization is obtained in a couple of milliseconds, independently of the number of microphones. Finally, we also propose different strategies to further accelerate the sound-source localization while offering increased angular resolution.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design Considerations When Accelerating an FPGA-Based Digital Microphone Array for Sound-Source Localization

et al. 2017

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“…In [2] the authors propose a beamforming-based acoustic system composed of up to 33 MEMS microphones for localization of the dominant noise source. The authors in [3] describe the design on an FPGA of an eight-element digital MEMS microphone array for distant speech recognition.…”

Section: Related Workmentioning

confidence: 99%

Runtime reconfigurable beamforming architecture for real-time sound-source localization

Silva

Segers

Braeken

et al. 2016

2016 26th International Conference on Field Programmable Logic and Applications (FPL)

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Abstract-Sound-source localization is used in many different real-time acoustic applications. Microphone arrays have the potential capability to recognize, profile and locate sound-sources in noisy environments. The quality response of such sensor arrays, however, is determined by the quantity of microphones. A higher number of microphones increases the computational demand, making real-time response challenging. In this paper, we present a scalable and runtime reconfigurable architecture to provide accurate sound-source localization in real-time. On one hand, the reconfigurable architecture is designed to be scalable in order to support a variable number of microphones. On the other hand, we use runtime reconfigurable look-up tables (CFGLUTs) to provide a dynamic response in real-time. Experiments demonstrate how an accurate sound-source localization is obtained in less than a few hundred milliseconds. As far as we are aware, it is the first time that runtime reconfiguration is applied to a reconfigurable architecture consisting of a sensor array. I. INTRODUCTIONMicrophone arrays are becoming popular to many different applications. The localization of sound-sources is applied in the environmental, industrial and military domains. For instance, military applications usually range from localizing sniper fire to identify noisy engine parts. The time response becomes crucial for those vital situations.Due to the recent miniaturization and price drop of good quality microphones in the form of Microelectromechanical systems (MEMS), microphone arrays are rapidly adopting such type of microphones. MEMS microphones are much cheaper than traditional microphones and have a relatively good signal-to-noise ratio (SNR) and frequency response. Their small size allows miniature arrays that are only several centimeters in diameter with a high level of integration.Most of the signal processing demanded by such arrays are traditionally computed in general-purpose processors. The computational demand, however, is directly related to the number of microphones of the array, which is intensely increasing thanks to the low-cost MEMS technology. Thanks to the highcomputational power and low latency response that FPGAs offer nowadays, we believe that FPGAs are not only able to manage relatively large microphone arrays but they also offer a faster response.With the additional number of microphones and real-time applications in mind, we propose a flexible, scalable and runtime reconfigurable architecture able to satisfy the most time demanding sound-source localization applications. Our main targets are the scalability of the system and the capability to dynamically reconfigure the microphone array in order to detect sound-sources in real-time (< 200ms).

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“…A digital MEMS (micro electro mechanical system) microphone is a microphone on a chip containing a pressure sensitive membrane, a matched pre-amplifier, and integrated analogue-digital conversion (ADC) and downsampling. We have previously constructed a prototype digital MEMS microphone array, DMMA.1 [6], and preliminary experiments produced promising results for a task based on the adaptation of WSJ acoustic models. DMMA.1 has a number of limitations, most significantly the inability to directly record all channels individually at 48kHz sample rate.…”

Section: Dmma2mentioning

confidence: 99%

On the effect of snr and superdirective beamforming in speaker diarisation in meetings

Zwyssig

Renals

Lincoln

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper examines the effect of sensor performance on speaker diarisation in meetings and investigates the use of more advanced beamforming techniques, beyond the typically employed delay-sum beamformer, for mitigating the effects of poorer sensor performance. We present superdirective beamforming and investigate how different time difference of arrival (TDOA) smoothing and beamforming techniques influence the performance of state-of-the-art diarisation systems. We produced and transcribed a new corpus of meetings recorded in the instrumented meeting room using a high SNR analogue and a newly developed low SNR digital MEMS microphone array (DMMA.2). This research demonstrates that TDOA smoothing has a significant effect on the diarisation error rate and that simple noise reduction and beamforming schemes suffice to overcome audio signal degradation due to the lower SNR of modern MEMS microphones.Index Terms-Speaker diarisation in meetings, digital MEMS microphone array, time difference of arrival (TDOA), superdirective beamforming

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A digital microphone array for distant speech recognition

Cited by 22 publications

References 21 publications

Design Considerations When Accelerating an FPGA-Based Digital Microphone Array for Sound-Source Localization

Design Considerations When Accelerating an FPGA-Based Digital Microphone Array for Sound-Source Localization

Runtime reconfigurable beamforming architecture for real-time sound-source localization

On the effect of snr and superdirective beamforming in speaker diarisation in meetings

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