Multichannel Audio Front-End for Far-Field Automatic Speech Recognition

Chhetri, A.S.; Hilmes, Philip; Kristjansson, Trausti T.; Chu, Wai C.; Mansour, M.; Li, Xiaoxue; Zhang, Xianxian

doi:10.23919/eusipco.2018.8553149

Cited by 20 publications

(12 citation statements)

References 6 publications

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“…In a future work, we describe a dynamic programming algorithm to prune the synchronization search by an order of magnitude with negligible impact on the detection performance. Future work also includes deploying microphone arrays [18] to improve self-correlation, and reduce the reverberation impact.…”

Section: Resultsmentioning

confidence: 99%

Audio Watermarking over the Air with Modulated Self-correlation

Tai

Mansour

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose a novel audio watermarking system that is robust to the distortion due to the indoor acoustic propagation channel between the loudspeaker and the receiving microphone. The system utilizes a set of new algorithms that effectively mitigate the impact of room reverberation and interfering sound sources without using dereverberation procedures. The decoder has low-latency and it operates asynchronously, which alleviates the need for explicit synchronization with the encoder. It is also robust to standard audio processing operations in legacy watermarking systems, e.g., compression and volume change. The effectiveness of the system is established with a real-time system under general room conditions.

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Section: Resultsmentioning

confidence: 99%

Audio Watermarking over the Air with Modulated Self-correlation

Tai

Mansour

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Regarding the number of microphones needed for UAV detection and the distance of the acoustic sensor from the UAV, past research [145] has shown that it is not possible to detect a drone’s sounds at distances greater than 150 m. Furthermore, studies regarding multi-channel audio [141] have shown that it is possible to significantly increase the performance of a detection framework, when using state-of-the-art beam-forming algorithms and fusion with other sensors (e.g., cameras). Regarding future research, it would be possible to transfer knowledge from the domain of speech recognition [178], using far-field signal processing techniques.…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review

Samaras

Diamantidou

Ataloglou

et al. 2019

Sensors

156

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Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks. However, this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c-UAV) applications. Nowadays, c-UAV applications offer systems that comprise a multi-sensory arsenal often including electro-optical, thermal, acoustic, radar and radio frequency sensors, whose information can be fused to increase the confidence of threat’s identification. Nevertheless, real-time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. To that end, many challenging tasks arise such as object detection, classification, multi-object tracking and multi-sensor information fusion. In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification is considered a novel concept. Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged. The aim of this paper is to describe deep learning advances on c-UAV related tasks when applied to data originating from many different sensors as well as multi-sensor information fusion. This survey may help in making recommendations and improvements of c-UAV applications for the future.

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“…The recent success of smart speakers in the consumer electronics market has created new opportunities and challenges for array signal processing [1]. This success is primarily attributed to advances in digital signal processing and machine learning systems [2].…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Acoustic Echo Cancellation Over Wireless Channels

Ayrapetian

Hilmes

Mansour

et al. 2021

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

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We introduce a novel acoustic echo cancellation framework for systems where the loudspeaker and the microphone array are not synchronized. We consider the problem in the most general form where the loss of synchronization is time-varying. The proposed system is linear and it utilizes microphone array beamforming for echo cancellation. It is shown to provide significant improvement over standard echo cancellation and noise suppression techniques in both noise suppression and speech recognition.

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Multichannel Audio Front-End for Far-Field Automatic Speech Recognition

Cited by 20 publications

References 6 publications

Audio Watermarking over the Air with Modulated Self-correlation

Audio Watermarking over the Air with Modulated Self-correlation

Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review

Asynchronous Acoustic Echo Cancellation Over Wireless Channels

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