Real time Artificial Auditory Systems for cluttered environments

Dentamaro, Giuseppe; Cardellicchio, Angelo; Guaragnella, Cataldo

doi:10.1109/icpr.2016.7899968

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…The single-microphone reception intensity I varies with the sound source in the normalized coordinate direction and is obtained through Equation (2), where p ( t ) is the intensity of the sound source and V is the particle velocity. Here, we set the V as 346 m/s based on the room temperature of 26 ℃ [ 27 ].

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Section: Methodsmentioning

confidence: 99%

A Real-Time Speech Separation Method Based on Camera and Microphone Array Sensors Fusion Approach

Liu

Ciou

Chen

et al. 2020

Sensors

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In the context of assisted human, identifying and enhancing non-stationary speech targets speech in various noise environments, such as a cocktail party, is an important issue for real-time speech separation. Previous studies mostly used microphone signal processing to perform target speech separation and analysis, such as feature recognition through a large amount of training data and supervised machine learning. The method was suitable for stationary noise suppression, but relatively limited for non-stationary noise and difficult to meet the real-time processing requirement. In this study, we propose a real-time speech separation method based on an approach that combines an optical camera and a microphone array. The method was divided into two stages. Stage 1 used computer vision technology with the camera to detect and identify interest targets and evaluate source angles and distance. Stage 2 used beamforming technology with microphone array to enhance and separate the target speech sound. The asynchronous update function was utilized to integrate the beamforming control and speech processing to reduce the effect of the processing delay. The experimental results show that the noise reduction in various stationary and non-stationary noise environments were 6.1 dB and 5.2 dB respectively. The response time of speech processing was less than 10ms, which meets the requirements of a real-time system. The proposed method has high potential to be applied in auxiliary listening systems or machine language processing like intelligent personal assistant.

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Section: Methodsmentioning

confidence: 99%