Abstract
Enhancing speech captured by distant microphones is a challenging task. In this study, we investigate the multichannel signal properties of the single acoustic vector sensor (AVS) to obtain the inter-sensor data ratio (ISDR) model in the time-frequency (TF) domain. Then, the monotone functions describing the relationship between the ISDRs and the direction of arrival (DOA) of the target speaker are derived. For the target speech enhancement (SE) task, the DOA of the target speaker is given, and the ISDRs are calculated. Hence, the TF components dominated by the target speech are extracted with high probability using the established monotone functions, and then, a nonlinear soft mask of the target speech is generated. As a result, a masking-based speech enhancement method is developed, which is termed the AVS-SMASK method. Extensive experiments with simulated data and recorded data have been carried out to validate the effectiveness of our proposed AVS-SMASK method in terms of suppressing spatial speech interferences and reducing the adverse impact of the additive background noise while maintaining less speech distortion. Moreover, our AVS-SMASK method is computationally inexpensive, and the AVS is of a small physical size. These merits are favorable to many applications, such as robot auditory systems.
Highlights
With the development of information technology, intelligent service robots will play an important role in smart home systems
The speech sources taken from the Institute of Electrical and Electronic Engineers (IEEE) speech corpus [26] are placed in the front of the acoustic vector sensor (AVS) at a distance of one meter, and the signal-to-interference ratio (SIR)-input is set to 0 dB, while the signal-to-noise ratio (SNR)-input is set to 10 dB, and the sampling rate was 48 kHz, and down-sampled to 16 kHz for processing
A nonlinear soft mask has been designed by making use of speech time-frequency (TF) sparsity with the known direction of arrival (DOA) of the target speaker
Summary
With the development of information technology, intelligent service robots will play an important role in smart home systems. Auditory perception is one of the key technologies of intelligent service robots [1]. It is clear that service robots are always working in noisy environments, and there are possible directional spatial interferences such as the competing speakers located in different locations, air conditioners, and so on. Additive background noise and spatial interferences significantly deteriorate the quality and intelligibility of the target speech, and speech enhancement (SE) is considered the most important preprocessing technique for speech applications such as automatic speech recognition [5]. The well-known single channel SE methods, including spectral subtraction, Wiener filtering, and their variations, are successful for suppressing additive background noise, but they are not able to suppress spatial interferences effectively [6].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
