VadNet is a real-time voice activity detector for noisy enviroments. It implements an end-to-end learning approach based on Deep Neural Networks. In the extended version, gender and laughter detection are added. To see a demonstration click on the images below. Windows.

The task of automatically detecting “Who is Speaking and When” is broadly named as Voice Activity Detection (VAD). Automatic VAD is a very important task and also the foundation of several domains, e.g., human-human, human-computer/ robot/ virtual-agent interaction analyses, and industrial applications.

2024年7月26日 · Silero VAD 是预训练的企业级语音端点检测模型，一个音频块 （30+ 毫秒） 在单个 CPU 线程上处理的时间不到 1 毫秒。 使用批处理或 GPU 也可以显著提高 性能。 在某些情况下，ONNX 的运行速度甚至可以提高 4-5 倍。

Voice activity detection (VAD), also known as speech activity detection or speech detection, is the detection of the presence or absence of human speech, used in speech processing. The main uses of VAD are in speech coding and speech recognition.

We present an automatic voice activity detection (VAD) method that is solely based on visual cues. Unlike traditional approaches processing audio, we show that upper body motion analysis is desirable for the VAD task.

2023年10月11日 · This paper proposes a real-time voice activity detection (VAD) system that utilizes a compressed convolutional neural network (CNN) model. On general-purpose computers, the system is capable of accurately classifying the presence of speech in audio with low latency.

2023年12月10日 · In the realm of digital audio processing, Voice Activity Detection (VAD) plays a pivotal role in distinguishing speech from non-speech elements, a task that becomes increasingly complex in noisy environments.

2024年4月26日 · 是一个基于深度学习的实时语音活动检测（Voice Activity Detection, VAD）项目，由HCMLab团队开发并开源。 该项目旨在帮助开发者和研究人员高效地识别音频流中的语音片段，提升语音处理与通信应用的性能。 VadNet的核心是一个轻量级、高效的神经网络模型，设计用于在各种环境噪声中准确区分语音和非语音段。 它适用于实时或离线处理大量音频数据，例如电话通话、在线会议、智能家居设备等应用场景。 1. 模型架构 VadNet采用了卷积神经网 …

2024年5月27日 · Voice activity detection (VAD) is the task of detecting speech in an audio stream, which is challenging due to numerous unseen noises and low signal-to-noise ratios in real environments. Recently, neural network-based VADs have alleviated the degradation of performance to some extent.

In this work, we present VADLite, an open-source, lightweight, system that performs real-time VAD on smartwatches. It extracts mel-frequency cepstral coeffi-cients and classifies speech versus non-speech audio samples using a linear Support Vector Machine. The real-time imple-mentation is done on the Wear OS Polar M600 smartwatch.