An automotive application of real-time adaptive Wiener filter for non-stationary noise cancellation in a car environment

Wiener filter is one of the most fundamental noise-reduction approaches among numerous techniques. The speech recognition in an in-vehicle environment needs a non-stationary noise cancellation to eliminate the background noise. However, few efforts have been reported to show the effectiveness of Wiener filter. Not much has been evaluated how the Wiener filter really works for reducing the non-stationary noise in real-time. In this paper, a real-time adaptive Wiener filter with two microphones is implemented to reduce noisy speech when noise signals and desired speech are incoming simultaneously. Furthermore, in order to build a real-time noise canceller, this paper also gives an analysis of different matrix sizes of the Wiener filter so as to enable the possibility of real-time implementation. The performance of the proposed design is measured by as much as 20dB noise reduction, and the proposed adaptive Wiener matrix update speed achieves a 28.6 ms/frame, with a matrix size of 200.