关键词: 声音事件识别, 正交匹配追踪, 粒子群优化, 短时谱估计, 深度置信网

Abstract:

A sound event recognition method based on optimized Orthogonal Matching Pursuit（OMP） and short-time spectrum estimation is proposed for decreasing the influence of sound event recognition on various environments. Firstly, Particle Swarm Optimization（PSO） is adopted to optimize OMP for sparse decomposition and reconstruction of sound signal to reserve the main body of sound signal. Secondly, the short-time spectrum estimation algorithm is employed to strengthen the residue signal after the first reconstruction and compensate the first reconstructed sound signal to reduce the influence of non-stationary noise and improve the precision of reconstructed sound signal. Then, an anti-noise composited feature of Mel Frequency Cepstrum Coefficient（MFCC）, time-frequency OMP feature, and Pitch feature is extracted from reconstructed signal, called OOMP feature. Finally, Deep Belief Networks（DBN） is employed to learn the OOMP feature and recognize 40 classes of sound events in different environment and SNR. The mean recognition rate can reach at 70.44% in different environment and SNR, and 49.9% even at ?5?dB, the experimental results show that the proposed method can effectively recognize sound events in various environments.

Key words: sound event recognition, Orthogonal Matching Pursuit（OMP）, Particle Swarm Optimization（PSO）, short-time spectrum estimation, Deep Belief Networks（DBN）