A method of high resolution frequency estimation based on a single vector sensor using ESPRIT （Estimating Signal Parameters via Rotational Invariance Techniques） algorithm is proposed and applied to the underwater a...A method of high resolution frequency estimation based on a single vector sensor using ESPRIT （Estimating Signal Parameters via Rotational Invariance Techniques） algorithm is proposed and applied to the underwater acoustic （UWA） communication system of frequency modulation. Higher resolution frequency estimation can be obtained by this algorithm using fewer snapshots comparing with the sound intensity frequency estimation. Results of simulation and lake experiment show that the proposed algorithm can improve the communication data rate and reduce the bandwidth of the system. Because higher signal-to-noise ratio （SNR） is demanded, range UWA communication at oresent. this algorithm can be used in high speed short展开更多
This paper presents a deep neural network(DNN)-based speech enhancement algorithm based on the soft audible noise masking for the single-channel wind noise reduction. To reduce the low-frequency residual noise, the ps...This paper presents a deep neural network(DNN)-based speech enhancement algorithm based on the soft audible noise masking for the single-channel wind noise reduction. To reduce the low-frequency residual noise, the psychoacoustic model is adopted to calculate the masking threshold from the estimated clean speech spectrum. The gain for noise suppression is obtained based on soft audible noise masking by comparing the estimated wind noise spectrum with the masking threshold. To deal with the abruptly time-varying noisy signals, two separate DNN models are utilized to estimate the spectra of clean speech and wind noise components. Experimental results on the subjective and objective quality tests show that the proposed algorithm achieves the better performance compared with the conventional DNN-based wind noise reduction method.展开更多
基金Supported by the Research on the Time Space Signal Processing Technology in the Underwater Acoustic Communication Foundation under Grant No. HEUF04081.
文摘A method of high resolution frequency estimation based on a single vector sensor using ESPRIT （Estimating Signal Parameters via Rotational Invariance Techniques） algorithm is proposed and applied to the underwater acoustic （UWA） communication system of frequency modulation. Higher resolution frequency estimation can be obtained by this algorithm using fewer snapshots comparing with the sound intensity frequency estimation. Results of simulation and lake experiment show that the proposed algorithm can improve the communication data rate and reduce the bandwidth of the system. Because higher signal-to-noise ratio （SNR） is demanded, range UWA communication at oresent. this algorithm can be used in high speed short
基金partially supported by the National Natural Science Foundation of China (Nos.11590772, 11590770)the Pre-research Project for Equipment of General Information System (No.JZX2017-0994/Y306)
文摘This paper presents a deep neural network(DNN)-based speech enhancement algorithm based on the soft audible noise masking for the single-channel wind noise reduction. To reduce the low-frequency residual noise, the psychoacoustic model is adopted to calculate the masking threshold from the estimated clean speech spectrum. The gain for noise suppression is obtained based on soft audible noise masking by comparing the estimated wind noise spectrum with the masking threshold. To deal with the abruptly time-varying noisy signals, two separate DNN models are utilized to estimate the spectra of clean speech and wind noise components. Experimental results on the subjective and objective quality tests show that the proposed algorithm achieves the better performance compared with the conventional DNN-based wind noise reduction method.
