【Title】A signal subspace dimension estimator based on F-norm with application to subspace-based multi-channel speech enhancement

【Author】LI Chao LIU Wenju (National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing 100190)

【Abstract】Although the signal subspace approach has been studied extensively for speech enhancement, no good solution has been found to identify signal subspace dimension in multichannel situation. This paper presents a signal subspace dimension estimator based on F-norm of correlation matrix, with which subspace-based multi-channel speech enhancement is robust to adverse acoustic environments such as room reverberation and low input signal to noise ratio (SNR).Experiments demonstrate the presented method leads to more noise reduction and less speech distortion comparing with traditional methods.