13 research outputs found
ViVoVAD: a Voice Activity Detection Tool based on Recurrent Neural Networks
Voice Activity Detection (VAD) aims to distinguishcorrectly those audio segments containing humanspeech. In this paper we present our latest approachto the VAD task that relies on the modellingcapabilities of Bidirectional Long Short TermMemory (BLSTM) layers to classify every frame inan audio signal as speech or non-speec
Array Configuration-Agnostic Personal Voice Activity Detection Based on Spatial Coherence
Personal voice activity detection has received increased attention due to the
growing popularity of personal mobile devices and smart speakers. PVAD is often
an integral element to speech enhancement and recognition for these
applications in which lightweight signal processing is only enabled for the
target user. However, in real-world scenarios, the detection performance may
degrade because of competing speakers, background noise, and reverberation. To
address this problem, we proposed to use equivalent rectangular bandwidth
ERB-scaled spatial coherence as the input feature to train an array
configuration-agnostic PVAD network. Whereas the network model requires only
112k parameters, it exhibits excellent detection performance and robustness in
adverse acoustic conditions. Notably, the proposed ARCA-PVAD system is scalable
to array configurations. Experimental results have demonstrated the superior
performance achieved by the proposed ARCA-PVAD system over a baseline in terms
of the area under receiver operating characteristic curve and equal error rate.Comment: Accepted by INTER-NOISE 2023. arXiv admin note: text overlap with
arXiv:2211.0874
Voice Activity Detection Using Deep Neural Network
13301甲第4842号博士(工学)金沢大学博士論文要旨Abstract 以下に掲載:Eurasip Journal on Audio, Speech and Music Processing 2018(1) pp.1-15 2018. Springer International Publishing. 共著者:Suci Dwijayanti, Kei Yamamori, Masato Miyosh