Implicit Wiener Filtering for Speech Enhancement In Non-Stationary Noise

Non-intrusive speech quality assessment using context-aware neural networks

AbstractTo meet the human perceived quality of experience (QoE) while communicating over various Voice over Internet protocol (VoIP) applications, for example Google Meet, Microsoft Skype, Apple FaceTime, etc. a precise speech quality assessment metric is needed. The metric should be able to detect and segregate different types of noise degradations present in the surroundings before measuring and monitoring the quality of speech in real-time. Our research is motivated by the lack of clear evidence presenting speech quality metric that can firstly distinguish different types of noise degradations before providing speech quality prediction decision. To that end, this paper presents a novel n…

Single-channel speech enhancement using implicit Wiener filter for high-quality speech communication

AbstractSpeech enables easy human-to-human communication as well as human-to-machine interaction. However, the quality of speech degrades due to background noise in the environment, such as drone noise embedded in speech during search and rescue operations. Similarly, helicopter noise, airplane noise, and station noise reduce the quality of speech. Speech enhancement algorithms reduce background noise, resulting in a crystal clear and noise-free conversation. For many applications, it is also necessary to process these noisy speech signals at the edge node level. Thus, we propose implicit Wiener filter-based algorithm for speech enhancement using edge computing system. In the proposed algor…