Recurrent Neural Network-based Microphone Howling Suppression

• Main Authors: LIN, CHENG-YANG, 林政陽
• Other Authors: LIAO, YUAN-FU
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/hd839v

碩士 === 國立臺北科技大學 === 電子工程系 === 107 === When using the karaoke system to sing, it is often too close the microphone and power of the amplified speaker is too large, causing a positive feedback and howling making the singer and the listener to be uncomfortable. Generally, to solve the microphone howling, often using a frequency shift to interrupt the resonance, or using a band-stop filter to remedy afterwards. But both may cause sound quality damage. Therefore, we want to use the adaptive feedback cancellation algorithm. Using the input source of the amplified speaker as the reference signal to automatically estimate the feedback signals that may record in different signal-to-noise. And eliminate the signal gain before howling occurs directly from the source. Based on the above ideas, in this paper, the howling elimination algorithm of normalized least mean square (NLMS) is realized, especially considering the nonlinear distortion of the sound amplification system, and the advanced algorithm based on recurrent neural network (RNN) is proposed. And in the experiment, test the time-domain or frequency-domain processing separately, and use NLMS or RNN, a total of four different combinations, the convergence speed and computational demand of different algorithms under different temperament and different environmental spatial response situations and howling suppression effect. The experimental results show that: (1) the convergence in the time domain is faster, (2) Stable effect in the frequency domain (3) Time domain RNN is best at eliminating effects, but there are too large calculations.