关键词: Hilbert-Huang变换, 内在模式分解, 独立子空间分析, C_均值算法

Abstract: The energy frequency distribution of non-stationary signal could not be got correctly with short-time Fourier transform. A new method was proposed to separate the audio sources from a single mixture based on Hilbert-Huang transform. Hilbert transform combined with Intrinsic Mode Functions (IMFs) constituted Hilbert Spectrum (HS) of mixture, which was a time-frequency representation of a non-stationary signal. The HS of mixture was used to derive the independent source subspaces. The time domain source signals were reconstructed by applying the inverse transformation. The simulated results show that the proposed method is efficient and improves the separation performance. It was observed that HS-based TF representation performed better than using STFT.

Key words: Hilbert-Huang transform, Empirical mode decomposition (EMD), independent subspace analysis (ISA), C_means