Forming ad-hoc microphone arrays through clustering of acoustic room impulse responses

Pasha, Shahab; Zou, Yue; Ritz, Christian

doi:10.1109/chinasip.2015.7230367

Cited by 5 publications

(10 citation statements)

References 18 publications

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“…Compact Unknown structure [9] Known structure Changeable microphone locations [10] Fixed topology Unknown inter-channel time delays [11] Known inter-channel time delays Inconsistent gain within the array [12] Consistent gain Uncertain direction of arrival (DOA) definition [13] Straightforward DOA definition Large phase differences (i.e., spatial aliasing)…”

Section: Distributedmentioning

confidence: 99%

“…Researchers have recently shown that the unique RIR and echo pattern at each ad hoc microphone location contain location and distance information even if the recording setup is unknown [32,33]. This idea is applicable to ad hoc microphone arrays for microphone clustering [11], room geometry reconstruction [33], and microphone localization [34].…”

Section: Room Acoustics and Distributed Recordingmentioning

confidence: 99%

“…As each ad hoc microphone receives its own unique distorted version of the source signal, the RIR and reverberation at each microphone location contain important information [11,35]. Each RIR (ℎ ( )) ( 6) can be represented by sets of reverberation times and reverberation amplitudes [11], as follows:…”

Section: Room Acoustics and Distributed Recordingmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Distributedmentioning

confidence: 99%

Section: Room Acoustics and Distributed Recordingmentioning

confidence: 99%

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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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“…Microphones with similar RIRs (similar time delays and amplitudes) are located close and can be grouped together as a cluster [7]. In the time domain a room impulse response from (1) can be represented mathematically as a truncated train of L (e.g.…”

Section: A Distributed Multi-node Recording Of Reverberant Speechmentioning

confidence: 99%

“…It was previously proposed by the authors that information derived from RIRs (time delay and gain attenuation) can indicate the relative distances between an active source and each microphone [7]. Although these derived cues are relative rather than being absolute, it is shown that if the room geometry is known, they can be utilised to localise an active source in a 2D plane accurately (assuming there is at least five randomly distributed single microphones in the room) [8].…”

Section: Introduction Imentioning

confidence: 99%

Detecting multiple, simultaneous talkers through localising speech recorded by ad-hoc microphone arrays

Pasha

Ritz

Zou

2016

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

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This paper proposes a novel approach to detecting multiple, simultaneous talkers in multi-party meetings using localisation of active speech sources recorded with an ad-hoc microphone array. Cues indicating the relative distance between sources and microphones are derived from speech signals and room impulse responses recorded by each of the microphones distributed at unknown locations within a room. Multiple active sources are localised by analysing a surface formed from these cues and derived at different locations within the room. The number of localised active sources per each frame or utterance is then counted to estimate when multiple sources are active. The proposed approach does not require prior information about the number and locations of sources or microphones. Synchronisation between microphones is also not required. A meeting scenario with competing speakers is simulated and results show that simultaneously active sources can be detected with an average accuracy of 75% and the number of active sources counted accurately 65% of the time.

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Exploiting Speaker Embeddings for Improved Microphone Clustering and Speech Separation in ad-hoc Microphone Arrays

Kindt

Thienpondt

Madhu

2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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For separating sources captured by ad hoc distributed microphones a key first step is assigning the microphones to the appropriate source-dominated clusters. The features used for such (blind) clustering are based on a fixed length embedding of the audio signals in a high-dimensional latent space. In previous work, the embedding was hand-engineered from the Mel frequency cepstral coefficients and their modulation-spectra. This paper argues that embedding frameworks designed explicitly for the purpose of reliably discriminating between speakers would produce more appropriate features. We propose features generated by the state-of-the-art ECAPA-TDNN speaker verification model for the clustering. We benchmark these features in terms of the subsequent signal enhancement as well as on the quality of the clustering where, further, we introduce 3 intuitive metrics for the latter. Results indicate that in contrast to the hand-engineered features, the ECAPA-TDNN-based features lead to more logical clusters and better performance in the subsequent enhancement stages -thus validating our hypothesis.

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Forming ad-hoc microphone arrays through clustering of acoustic room impulse responses

Cited by 5 publications

References 18 publications

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

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

Detecting multiple, simultaneous talkers through localising speech recorded by ad-hoc microphone arrays

Exploiting Speaker Embeddings for Improved Microphone Clustering and Speech Separation in ad-hoc Microphone Arrays

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