A real-world recording database for ad hoc microphone arrays

Woods, William S.; Hadad, Elior; Merks, Ivo; Xu, Buye; Gannot, Sharon; Zhang, Tao

doi:10.1109/waspaa.2015.7336915

Cited by 15 publications

(12 citation statements)

References 13 publications

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“…i.e., the output signals of the BMVDR-N beamformer can be interpreted as a mixture of the output signals of the BMVDR beamformer (scaled with 1 − η) and the noisy reference microphone signals (scaled with η). For η = 0, the BMVDR-N beamformer in (39) and ( 40) is equal to the BMVDR beamformer in (32) and (33). For η = 1, only the reference microphone signals are used, i.e., no beamforming is applied at all.…”

Section: B Bmvdr Beamformer With Partial Noise Estimation (Bmvdr-n)mentioning

confidence: 99%

“…The derived analytical expressions are firstly validated using simulated anechoic acoustic transfer functions (ATFs), where the listener's head is modelled as a rigid sphere [32]. Experiments are then performed using recorded signals for a binaural hearing device configuration in a reverberant environment with multiple interfering talkers as background noise [33]. The experimental results show that in a realistic scenario, incorporating an external microphone in the BMVDR-N beamformer significantly increases the output SNR and decreases the mixing parameter required to obtain a desired output MSC, i.e., the spatial impression, of the noise component for different positions of the external microphone and the desired source.…”

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confidence: 99%

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Performance Analysis of the Extended Binaural MVDR Beamformer With Partial Noise Estimation

Gosling

Marquardt

Doclo

2021

IEEE/ACM Trans. Audio Speech Lang. Process.

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Besides reducing undesired noise sources and limiting speech distortion, another important objective of a binaural noise reduction algorithm is the preservation of the binaural cues of all sound sources in the acoustic scene. In this paper, we consider the binaural minimum variance distortionless response beamformer with partial noise estimation (BMVDR-N), which allows to trade off between noise reduction performance and binaural cue preservation of the noise component by mixing the output signals of the BMVDR beamformer with the noisy reference microphone signals.For a directional noise source, it has been shown that incorporating an external microphone in addition to the head-mounted microphones enables both the noise reduction performance as well as the interaural time and level difference cues of the noise component to be improved in the output signals. In this paper, we consider an arbitrary noise field and analytically show that incorporating an external microphone in the BMVDR-N beamformer enables 1) a larger output signal-to-noise ratio (SNR) for the same mixing parameter, 2) the same output SNR for a larger mixing parameter, and 3) the same desired output magnitude squared coherence (MSC) of the noise component for a smaller mixing parameter to be obtained. The derived analytical expressions are firstly validated using simulated anechoic acoustic transfer functions, where the listener's head is modelled as a rigid sphere. Experimental results using recorded signals for a binaural hearing device setup in a reverberant environment also show that in a realistic scenario incorporating an external microphone in the BMVDR-N beamformer significantly improves the output SNR and reduces the mixing parameter that is required to obtain a desired output MSC of the noise component compared to using only the head-mounted microphones.

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Section: B Bmvdr Beamformer With Partial Noise Estimation (Bmvdr-n)mentioning

confidence: 99%

mentioning

confidence: 99%

Performance Analysis of the Extended Binaural MVDR Beamformer With Partial Noise Estimation

Gosling

Marquardt

Doclo

2021

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…The LCMV was applied to distributed scenarios by Wood et al [81] in experimental setups covering a wide range of random meeting scenarios. However, the LCMV beamformer requires knowledge of the RIR at each microphone location for each source in the meeting room.…”

Section: Linearly Constrained Minimum Variance (Lcmv)mentioning

confidence: 99%

“…An extensive database recorded using ad hoc microphones was reported and applied by Wood et al [81] for real-world beamforming experiments. Twenty-four microphones were positioned in various locations on a central table in a reverberant room, and their outputs were recorded while four target talkers seated at the table read some text or had natural conversations.…”

Section: Distributed Signal Processing Resourcesmentioning

confidence: 99%

Distributed Microphone Arrays, Emerging Speech and Audio Signal Processing Platforms: A Review

Pasha¹,

Lundgren²,

Ritz³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Given ubiquitous digital devices with recording capability, distributed microphone arrays are emerging recording tools for hands-free communications and spontaneous tele-conferencings. However, the analysis of signals recorded with diverse sampling rates, time delays, and qualities by distributed microphone arrays is not straightforward and entails important considerations. The crucial challenges include the unknown/changeable geometry of distributed arrays, asynchronous recording, sampling rate mismatch, and gain inconsistency. Researchers have recently proposed solutions to these problems for applications such as source localization and dereverberation, though there is less literature on real-time practical issues. This article reviews recent research on distributed signal processing techniques and applications. New applications benefitting from the wide coverage of distributed microphones are reviewed and their limitations are discussed. This survey does not cover partially or fully connected wireless acoustic sensor networks.

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“…Joint analysis of the ad-hoc microphones is challenging as the microphones are usually not synchronised, the array topology is unknown and potentially changeable and the microphones have different gains and qualities [6]. Due to such problematic limitations joint analysis of the ad-hoc microphones usually requires assumptions such as the knowledge of the number of sources [7] or the Room Impulse Responses (RIRs) at each microphone location [8].…”

Section: Introductionmentioning

confidence: 99%

Multi-Channel Compression and Coding of Reverberant Ad-Hoc Recordings Through Spatial Autoregressive Modelling

Pasha

Lundgren

2019

2019 30th Irish Signals and Systems Conference (ISSC)

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Autoregressive modelling techniques such as multi-channel linear prediction are widely used for applications such as coding, dereverberation and compression of the speech signals. State of the art multi-channel linear prediction methods do not take into account the spatial locations of the microphones and assume single distance compact microphone arrays. In this paper a spatially modified multichannel autoregressive compression and coding method is proposed and successfully tested in order to adapt the standard multi-channel method to the virtual reality and immersive video conferencing applications where the microphones can be meters away from each other. The proposed method estimates the spatial distances between each microphone and the source to optimise the joint compression of the microphones covering a wide area. The results suggest that the proposed method outperforms the standard multi-channel compression and coding method when applied to the ad-hoc scenarios.

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A real-world recording database for ad hoc microphone arrays

Cited by 15 publications

References 13 publications

Performance Analysis of the Extended Binaural MVDR Beamformer With Partial Noise Estimation

Performance Analysis of the Extended Binaural MVDR Beamformer With Partial Noise Estimation

Distributed Microphone Arrays, Emerging Speech and Audio Signal Processing Platforms: A Review

Multi-Channel Compression and Coding of Reverberant Ad-Hoc Recordings Through Spatial Autoregressive Modelling

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