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Keywords = TF soft mask

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15 pages, 5687 KiB  
Article
An Underdetermined Convolutional Blind Separation Algorithm for Time–Frequency Overlapped Wireless Communication Signals with Unknown Source Number
by Hao Ma, Xiang Zheng, Lu Yu, Xinrong Wu and Yu Zhang
Appl. Sci. 2022, 12(13), 6534; https://doi.org/10.3390/app12136534 - 28 Jun 2022
Viewed by 1487
Abstract
It has been challenging to separate the time–frequency (TF) overlapped wireless communication signals with unknown source numbers in underdetermined cases. In order to address this issue, a novel blind separation strategy based on a TF soft mask is proposed in this paper. Based [...] Read more.
It has been challenging to separate the time–frequency (TF) overlapped wireless communication signals with unknown source numbers in underdetermined cases. In order to address this issue, a novel blind separation strategy based on a TF soft mask is proposed in this paper. Based on the clustering property of the signals in the sparse domain, the angular probability density distribution is obtained by the kernel density estimation (KDE) algorithm, and then the number of source signals is identified by detecting the peak points of the distribution. Afterward, the contribution degree function is designed according to the cosine distance to calculate the contribution degrees of the source signals in the mixed signals. The separation of the TF overlapped signals is achieved by constructing a soft mask matrix based on the contribution degrees. The simulations are performed with digital signals of the same modulation and different modulation, respectively. The results show that the proposed algorithm has better anti-aliasing and anti-noise performance than the comparison algorithms. Full article
(This article belongs to the Special Issue Advanced Electronics and Digital Signal Processing)
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17 pages, 3465 KiB  
Article
Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
by Yuexian Zou, Zhaoyi Liu and Christian H. Ritz
Appl. Sci. 2018, 8(9), 1436; https://doi.org/10.3390/app8091436 - 23 Aug 2018
Cited by 8 | Viewed by 3174
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Modelling, Simulation and Data Analysis in Acoustical Problems)
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