Robust acoustic feature extraction for sound classification based on noise reduction

In this paper, we present a novel method for environmental sound classification in non-stationary noise environment. The proposed method mainly consists of three stages: noise source separation and acoustic feature extraction and multi-class classification. At first stage, we employ probabilistic latent component analysis (PLCA) to perform time-varying noise separation. To alleviate the artifacts introduced by source separation, a series of spectral weightings is applied to enhance reliability of audio spectra. At feature extraction stage, we extract acoustic subspace to effectively characterize temporal-spectral patterns of denoised sound spectrogram. Subsequently, regularized kernel Fisher discriminant analysis (KFDA) is adopted to conduct multi-class sound classification through exploiting class conditional distributions based on extracted acoustic subspaces (features). The proposed method is evaluated with Real World Computing Partnership (RWCP) sound scene database and experimental results demonstrate its superior performance compared to other methods.