Multiband subspace tracking speech enhancement for in-car human computer speech interaction

In this paper, a new subspace-based speech enhancement algorithm for in-car human computer speech interaction is presented. We first incorporate a perceptual filter-bank which is derived from psycho-acoustic model with signal subspace approach to effectively suppress in-car noises of engine. Second, for real-time applications, a new subspace tracking algorithm is derived by modifying PASTd algorithm to solve the data dependent hazard of tacking algorithm. Six different types of in-car noises in TAICAR database are used in our evaluation. The experimental results demonstrate that our approach is superior to conventional subspace and spectral subtraction methods.