A signal subspace approach for speech enhancement

• Main Author: Geng, Xiuhua
• Other Authors: Soon Ing Yann
• Format: Theses and Dissertations
• Language: English
• Published: 2015
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
• Online Access: http://hdl.handle.net/10356/64805
• Institution: Nanyang Technological University

Speech enhancement aims to improve the performance of speech processing systems operating in various noisy environments. The performance of speech enhancement algorithm can be evaluated by two uncorrelated criteria: clarity and intelligibility. Here, we present a speech enhancement algorithm based on the signal subspace method, which can be adopted for arbitrary noise types. Firstly, an equalizer is introduced to whiten the noise embedded in the noisy speech signal. Then, by applying the Karhunen-Loeve transform (KLT), the noisy speech signal is decomposed into two subspaces: noise subspace and signal-plus-noise subspace. The clean signal can be estimated from the signal-plus-noise subspace after eliminating the noise subspace. By assuming the noise is additive and uncorrelated with clean signal, the recovery of the original signal is conducted frame-by-frame by introducing two criteria: Time Domain Constrained (TDC) and Spectral Domain Constrained (SDC). TDC is used to alleviate the signal distortion when the energy of the residual noise is below a certain threshold. SDC can be utilized to minimize the signal distortion under a fixed spectrum of the residual noise. Simulation results show that our proposed algorithm is able to deal with the arbitrary noise types effectively. Index Terms- Karhunen-Loeve transform, TDC, SDC, signal subspace, speech enhancement.