Fusing the advantages of multiple acoustic features is important for the robustness of voice
activity detection (VAD). Recently, the machine-learning-based VADs have shown a …

This paper presents a review of multi-objective deep learning methods that have been
introduced in the literature for speech denoising. After stating an overview of conventional …

Voice activity detection (VAD) is an important topic in audio signal processing. Contextual
information is important for improving the performance of VAD at low signal-to-noise ratios …

Voice activity detection (VAD) classifies incoming signal segments into speech or
background noise; its performance is crucial in various speech-related applications …

Voice activity detection is an essential pre-processing component for speech-related tasks
such as automatic speech recognition (ASR). Traditional supervised VAD systems obtain …

In this paper, a signal processing approach is proposed for speech/nonspeech
discrimination. The approach is based on single frequency filtering (SFF), where the …

Voice Activity Detection (VAD) refers to the problem of distinguishing speech segments from
background noise. Numerous approaches have been proposed for this purpose. Some are …

This article presents a voice and acoustic activity detector that uses a mixer-based
architecture and ultra-low-power neural network (NN)-based classifier. By sequentially …

Voice activity detection (VAD) is an important frontend of many speech processing systems.
In this paper, we describe a new VAD algorithm based on boosted deep neural networks …

This paper presents an ultra-low-power voice activity detection (VAD) system to discriminate
speech from non-speech parts of audio signals. The proposed VAD system uses level …