Acoustic Echo Cancellation Based on Recurrent Neural Network
| Autor: | Yao-Cheng Tsai, 蔡曜丞 |
|---|---|
| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 Acoustic echo cancellation is a common problem in speech and signal processing until now. Application scenarios such as telephone conference, hands-free handsets and mobile communications. In the past we used adaptive filters to deal with acoustic echo cancellation, and today we can use deep learning to solve complex problems in acoustic echo cancellation. The method proposed in this work is to consider acoustic echo cancellation as a problem of speech separation, instead of the traditional adaptive filter to estimate acoustic echo. And use the recurrent neural network architecture in deep learning to train the model. Since the recurrent neural network has a good ability to simulate time-varying functions, it can play a role in solving the problem of acoustic echo cancellation. We train a bidirectional long short-term memory network and a bidirectional gated recurrent unit. Features are extracted from single-talk speech and double-talk speech. Adjust weights to control the ratio between double-talk speech and single-talk speech, and estimate the ideal ratio mask. This way to separate the signal, in order to achieve the purpose of removing the echo. The experimental results show that the method has good effect in echo cancellation. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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