Design and Implementation of Subspace-Based Speech Enhancement Under In-Car Noisy Environments

摘要

In this paper, a new subspace-based speech enhancement model is presented for in-car speech enhancement. To effectively suppress background noise, this model incorporates a perceptual filterbank and an auditory gain adaptation derived from a psychoacoustic model into a signal subspace approach. The projection approximation subspace tracking deflation (PASTd) algorithm is used to track the signal subspace. For real-time processing, a system-on-a-programmable-chip architecture and a very large scale integration design of the PASTd algorithm are proposed. To realize a pipeline computation, this paper presents a pipelined PASTd architecture without data-dependent hazards. The maximum clock rate is 9.7 MHz, and the typical clock rate, which achieves the real-time requirement, is 4.6 MHz. The corresponding architecture was experimentally verified via an ALTERA EPXA10 development board.