Speech Enhancement with Two Microphones Based on Recurrent Neural Network
Large-scale neural networks are difficult to deploy in hearing aids. A dual microphone speech enhancement algorithm based on recurrent neural networks( RNN) is proposed,which has the advantages of low latency and low complexity. The algorithm uses two micro-phones for beamforming to preliminarily filter out the unexpected directional noise,and further obtains the pure voice signal through RNN. In order to solve the problem of large group delay caused by excessive order of filter banks,a piecewise all phase filter bank is proposed, which can reduce the group delay and computation. The simulation results show that the group delay is 3. 125 ms under 16 k sampling rate,and under the 0 dB babble,volvo,and factory1 environment,the SNR has increased by 10.5565 dB,and the PESQ has increased by 0.6787. In the actual test results,the SNR has increased by 9.4394 dB,and the PESQ has increased by 0.7350. When the clock frequen-cy is 104 MHz,the DSP system delay of the hearing aid is 8.4 ms and the power consumption is 6.2 mA.
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维普
