Speech Enhancement Framework with Noise Suppression Using Block Principal Component Analysis

With the advancement in voice-communication-based human-machine interface technology in smart home devices, the ability to decompose the received speech signal into a signal of interest and an interference component has emerged as a key requirement for their successful operation. These devices perform their tasks in real time based on the received commands, and their effectiveness is limited when there is a lot of ambient noise in the area in which they operate. Most real-time speech enhancement algorithms do not perform adequately well in the presence of high amounts of noise (i.e., low input-signal-to-noise ratio). In this manuscript, we propose a speech enhancement framework to help these algorithms in situations when the noise level in the received signal is high. The proposed framework performs noise suppression in the frequency domain by generating an approximation of the noisy signals' short-time Fourier transform, which is then used by the speech enhancement algorithms to recover the underlying clean signal. This approximation is performed by using the proposed block principal component analysis (Block-PCA) algorithm. To illustrate efficacy of the proposed framework, we present a detailed performance evaluation under different noise levels and noise types, highlighting the effectiveness of the proposed framework. Moreover, the proposed method can be used in conjunction with any speech enhancement algorithm to improve its performance under moderate to high noise scenarios.