基于Hilbert-Huang变换的语音信号分离

针对短时傅里叶变换不能正确得到非平稳信号的能量频率分布问题，提出了一种基于Hilbert-Huang变换的单信道语音信号分离的算法。该算法首先对分解得到的各内蕴模式函数分量(IMF)进行Hilbert变换，得到混合信号时频面上的Hilbert谱，然后对混合信号的Hilbert谱运用独立子空间分析的方法得出代表各个独立源信号的子空间，并对其求逆变换，从而恢复出各个源信号。通过仿真实验验证了此算法的正确性和有效性，且与短时傅里叶变换时频分析法相比较，其分离性能明显得到改善，显示了Hilbert-Huang变换在处理非平稳信号的优越性。

Audio source separation based on Hilbert-Huang transform

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.