Sparse parametric modeling of the early part of acoustic impulse responses

Papayiannis, Constantinos; Evers, Christine; Naylor, Patrick A.

doi:10.23919/eusipco.2017.8081293

Cited by 11 publications

(9 citation statements)

References 18 publications

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“…We prove the theorem by contradiction. Assume that the filters are not SL-coprime and have a decomposition as in (6). Then the output signals are decomposed as…”

Section: Theorem 1 (Ct-mbd Necessary Conditionmentioning

confidence: 99%

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Sub-NYQUIST Multichannel Blind Deconvolution

Mulleti

Lee

Eldar

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We consider a continuous-time sparse multichannel blind deconvolution problem. The signal at each channel is expressed as the convolution of a common source signal and its impulse response given as a sparse filter. The objective is to identify these sparse filters from sub-Nyquist samples of channel outputs by leveraging the correlation across channels. We present necessary and sufficient conditions for the unique identification. In particular, the sparse filters should not share a common sparse convolution factor and it is necessary to have 2L or more samples per channel from at least two distinct channels. We also show that L-sparse filters are uniquely identifiable from two channels provided that there are 2L 2 Fourier measurements per channel, which can be computed from sub-Nyquist samples. Additionally, in the asymptotic of the number of channels, 2L Fourier measurements per channel are sufficient. The results are applicable to the design of multi-receiver, low-rate, sensors in applications such as radar, sonar, ultrasound, and seismic exploration.

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“…We prove the theorem by contradiction. Assume that the filters are not SL-coprime and have a decomposition as in (6). Then the output signals are decomposed as…”

Section: Theorem 1 (Ct-mbd Necessary Conditionmentioning

confidence: 99%

“…0100101; and NSF under award CCF-1718771 and CAREER award CCF-1943201. [5], room impulse response modeling [6], sonar imaging [7], and ultrasound imaging [8,9]. In these applications, a source signal is reflected from sparsely located targets and the reflected signal is observed from multiple receivers.…”

Section: Introductionmentioning

confidence: 99%

Sub-NYQUIST Multichannel Blind Deconvolution

Mulleti

Lee

Eldar

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…, 7}. The AIRhm(n) is generated using the model described in [18]. The direct path sound Time Difference of Arrival (TDoA) for the Q=7 receivers is contained based on the array architecture.…”

Section: Data Augmentationmentioning

confidence: 99%

Detecting Media Sound Presence in Acoustic Scenes

et al. 2018

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Using speech to interact with electronic devices and access services is becoming increasingly common. Using such applications in our households poses new challenges for speech and audio processing algorithms as these applications should perform robustly in a number of scenarios. Media devices are very commonly present in such scenarios and can interfere with the user-device communication by contributing to the noise or simply by being mistaken as user issued voice commands. Detecting the presence of media sounds in the environment can help avoid such issues. In this work we propose a method for this task based on a parallel CNN-GRU-FC classifier architecture which relies on multi-channel information to discriminate between media and live sources. Experiments performed using 378 hours of in-house audio recordings collected by volunteers show an F1 score of 71% with a recall of 72% in detecting active media sources. The use of information from multiple channels gave a relative improvement of 16% to the F1 score when compared to using information from only a single channel.

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“…With an unknown source, the problem of determining sparse filters is known as sparse multichannel blind-deconvolution (S-MBD). This model is ubiquitous in many other applications such as seismic signal processing [3], room impulse response modeling [4], sonar imaging [5], and ultrasound imaging [6,7]. In these applications, the receivers' hardware and computational complexity depend on the number of measurements required at each receiver or channel to determine sparse filters uniquely.…”

Section: Introductionmentioning

confidence: 99%

Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution

Tolooshams

Mulleti

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution. In this problem, each channel's measurements are given as convolution of a common source signal and sparse filter. Unlike prior works where the compression is achieved either through random projections or by applying a fixed structured compression matrix, this paper proposes to learn the compression matrix from data. Given the full measurements, the proposed network is trained in an unsupervised fashion to learn the source and estimate sparse filters. Then, given the estimated source, we learn a structured compression operator while optimizing for signal reconstruction and sparse filter recovery. The efficient structure of the compression allows its practical hardware implementation. The proposed neural network is an autoencoder constructed based on an unfolding approach: upon training, the encoder maps the compressed measurements into an estimate of sparse filters using the compression operator and the source, and the linear convolutional decoder reconstructs the full measurements. We demonstrate that our method is superior to classical structured compressive sparse multichannel blind-deconvolution methods in terms of accuracy and speed of sparse filter recovery.

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Sparse parametric modeling of the early part of acoustic impulse responses

Cited by 11 publications

References 18 publications

Sub-NYQUIST Multichannel Blind Deconvolution

Sub-NYQUIST Multichannel Blind Deconvolution

Detecting Media Sound Presence in Acoustic Scenes

Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution

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