An invertible frequency eigendomain transformation for masking-based subspace speech enhancement

Masking properties have been widely exploited in speech enhancement techniques, especially those implemented in the spectral domain. The incorporation of auditory masking in a subspace technique invariably requires a transformation linking the frequency and eigendomains. In this letter, an invertible transformation between the frequency and eigendomains is derived. The proposed transformation is verified through a conventional masking-based subspace speech-enhancement method. Simulation results show that our proposed transformation for speech enhancement outperforms the conventional transformation in terms of segmental signal-to-noise ratio (SNR), perceptual evaluation of speech quality (PESQ), and listening tests.