A dynamic multi-channel speech enhancement system for distributed microphones in a car environment

Matheja, Timo; Buck, Markus; Fingscheidt, Tim

doi:10.1186/1687-6180-2013-191

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…The former applications are designed for communication with external parties and require speech enhancement, e.g. beamforming or noise reduction [1][2][3]. On the other hand, ICC systems are designed to enhance speech intelligibility between passengers by reinforcing desired speech signals in the car cabin [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Model-Based Estimation of in-Car-Communication Feedback Applied to Speech Zone Detection

Müller¹,

Doclo

Østergaard

et al. 2022

2022 International Workshop on Acoustic Signal Enhancement (IWAENC)

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Modern cars provide versatile tools to enhance speech communication. While an in-car communication (ICC) system aims at enhancing communication between the passengers by playing back desired speech via loudspeakers in the car, these loudspeaker signals may disturb a speech enhancement system required for hands-free telephony and automatic speech recognition. In this paper, we focus on speech zone detection, i.e. detecting which passenger in the car is speaking, which is a crucial component of the speech enhancement system. We propose a modelbased feedback estimation method to improve robustness of speech zone detection against ICC feedback. Specifically, since the zone detection system typically does not have access to the ICC loudspeaker signals, the proposed method estimates the feedback signal from the observed microphone signals based on a free-field propagation model between the loudspeakers and the microphones as well as the ICC gain. We propose an efficient recursive implementation in the short-time Fourier transform domain using convolutive transfer functions. A realistic simulation study indicates that the proposed method allows to increase the ICC gain by about 6 dB while still achieving robust speech zone detection results.

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Section: Introductionmentioning

confidence: 99%

Model-Based Estimation of in-Car-Communication Feedback Applied to Speech Zone Detection

Müller¹,

Doclo

Østergaard

et al. 2022

2022 International Workshop on Acoustic Signal Enhancement (IWAENC)

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“…For the CTC task, a multi-channel meeting scenario was designed towards an ad-hoc sensor network [4,2] where we only have access to the sensor observations of all other users, which are as well corrupted by crosstalk. In this context, adaptive filtering in time and frequency domain has been adopted to estimate the room impulse responses (RIRs) from the interfering source's microphone to the target microphone [5,6,7]. However, it requires a speaker activity detector or prior knowledge of the signal-to-interference ratio (SIR) to control the adaptation process.…”

Section: Introductionmentioning

confidence: 99%

Cancellation of Local Competing Speaker with Near-Field Localization for Distributed ad-hoc Sensor Network

Zarazaga¹,

Mansali²,

Bäckström³

et al. 2021

Interspeech 2021

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In scenarios such as remote work, open offices and call centers, multiple people may simultaneously have independent spoken interactions with their devices in the same room. The speech of competing speakers will however be picked up by all microphones, both reducing the quality of audio and exposing speakers to breaches in privacy. We propose a cooperative cross-talk cancellation solution breaking the single active speaker assumption employed by most telecommunication systems. The proposed method applies source separation on the microphone signals of independent devices, to extract the dominant speaker in each device. It is realized using a localization estimator based on a deep neural network, followed by a time-frequency mask to separate the target speech from the interfering one at each time-frequency unit referring to its orientation. By experimental evaluation, we confirm that the proposed method effectively reduces crosstalk and exceeds the baseline expectation maximization method by 10 dB in terms of interference rejection. This performance makes the proposed method a viable solution for cross-talk cancellation in near-field conditions, thus protecting the privacy of external speakers in the same acoustic space.

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“…In contrast, some researchers pay more attention on performing SE on the architecture of distributed-microphones [13,14,15,16]. This physical configuration, consisting of many individual selfpowered microphones or microphone arrays, can be deployed in a large area [17,18].…”

Section: Introductionmentioning

confidence: 99%

Distributed Microphone Speech Enhancement based on Deep Learning

Wang

Liang²,

Hung

et al. 2019

Preprint

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Speech-related applications deliver inferior performance in complex noise environments. Therefore, this study primarily addresses this problem by introducing speech-enhancement (SE) systems based on deep neural networks (DNNs) applied to a distributed microphone architecture. The first system constructs a DNN model for each microphone to enhance the recorded noisy speech signal, and the second system combines all the noisy recordings into a large feature structure that is then enhanced through a DNN model. As for the third system, a channel-dependent DNN is first used to enhance the corresponding noisy input, and all the channel-wise enhanced outputs are fed into a DNN fusion model to construct a nearly clean signal. All the three DNN SE systems are operated in the acoustic frequency domain of speech signals in a diffuse-noise field environment. Evaluation experiments were conducted on the Taiwan Mandarin Hearing in Noise Test (TMHINT) database, and the results indicate that all the three DNN-based SE systems provide the original noise-corrupted signals with improved speech quality and intelligibility, whereas the third system delivers the highest signal-to-noise ratio (SNR) improvement and optimal speech intelligibility.

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A dynamic multi-channel speech enhancement system for distributed microphones in a car environment

Cited by 9 publications

References 25 publications

Model-Based Estimation of in-Car-Communication Feedback Applied to Speech Zone Detection

Model-Based Estimation of in-Car-Communication Feedback Applied to Speech Zone Detection

Cancellation of Local Competing Speaker with Near-Field Localization for Distributed ad-hoc Sensor Network

Distributed Microphone Speech Enhancement based on Deep Learning

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