Speech enhancement via Mel-scale Wiener filtering with a frequency-wise voice activity detector

Abstract

This paper presents a speech enhancement system that enables a comfortable communication inside an automobile. A couple of novel concepts are proposed in an effort to improve two major building blocks in the existing speech enhancement systems: a voice activity detector (VAD) and a noise filtering algorithm. The proposed VAD classifies a given data frame as speech or noise at each frequency, enabling the frequency-wise updates of noise statistics and thereby improving the effectiveness of the noise filtering algorithms by providing more up-to-date noise statistics. The celebrated Wiener filter is adopted in this paper as the accompanying noise filtering algorithm, which results in significant noise suppression. Yet, the musical noise present in most Wiener filter-based systems prompts the idea of applying the Wiener filter in the Mel-scale in which the human auditory system responds to the external stimulation. It turns out that the Mel-scale Wiener filter creates some masking effects and thereby reduces musical noise significantly, leading to smooth transition between data frames.