Self-Attentive VAD: Context-Aware Detection of Voice from Noise

Abstract

Recent voice activity detection (VAD) schemes have aimed at leveraging the decent neural architectures, but few were successful with applying the attention network due to its high reliance on the encoder-decoder framework. This has often let the built systems have a high dependency on the recurrent neural networks, which are costly and sometimes less context-sensitive considering the scale and property of acoustic frames. To cope with this issue with the selfattention mechanism and achieve a simple, powerful, and environment-robust VAD, we first adopt the self-attention architecture in building up the modules for voice detection and boosted prediction. Our model surpasses the previous neural architectures in view of low signal-to-ratio and noisy real-world scenarios, at the same time displaying the robustness regarding the noise types. We make the test labels on movie data publicly available for the fair competition and future progress.

An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis

“…In summary, although existing methods can detect audio copy-move forgery, there are still some issues or limitations that deserve attention and improvement. Existing passive forensics methods overly rely on segmentation techniques to segment audio into silent and voiced segments, but audio collected in noisy environments is often difficult to segment [18,19]. These methods require repeatedly adjusting the similarity threshold to make feature similarity decisions in order to determine whether there are copy-move forged segments in the audio.…”