Speech enhancement is a fundamental way to improve speech perception quality in adverse environment where the received speech is seriously corrupted by noise. In this paper, we propose a cognitive computing based speech enhancement model termed SETransformer which can improve the speech quality in ...
人工智能热...发表于人工智能热... CMGAN:用于语音增强的基于Conformer的Metric GAN CMGAN: Conformer-based Metric GAN for Speech Enhancement Abstract——最近,卷积增强Conformer在自动语音识别(ASR)和时域语音增强(SE)中取得了可喜的表现,因为它可以捕捉到语音信… RainM...发表于语音增强论...打开...
TSTNN: TWO-STAGE TRANSFORMER BASED NEURAL NETWORK FOR SPEECH ENHANCEMENT IN THE TIME DOMAIN ABSTRACT——在本文中,我们提出了一个基于transformer的架构,称为两级transformer神经网络(TSTNN),用于时域的端到端语音去噪。提出的模型由一个编码器、一个两级transformer模块(TSTM)、一个掩码模块和一个解码器组成。编...
如文献[14]利用深度学习领域新颖的Transformer模型构建了语音增强Transformer(Speech Enhancement Transformer,SETransformer)网络,提出了一种基于多头注意力机制的语音增强技术,推理速度快于标准的长短期记忆(Long Short-Term Memory,LSTM)人工神经网络,可以有...
Speech enhancement (SE) aims to improve the quality and intelligibility of speech signals, particularly in the presence of noise or other distortions, to ensure reliable communication and robust speech recognition. Deep neural networks (DNNs) have shown remarkable success in SE due to their ability ...
该文提出了一种解耦式语音增强网络DPST-SENet (Dual-Path Skip-Transformer Speech enhancement network).具体而言,DPST-SENet 能够在幅度分支中抑制主要噪声分量,同时在复频谱分支中消除残余噪声并隐式增强相位信息.该网络引入Dual-Path Skip-Transformer模块,它能有效重用Dual-Path Transformer模块建模的信息,在降低参数...
Hansen “Speech Enhancement for Cochlear Implant using Deep Complex Convolution Transformer with Frequency Transformation, IEEE Transaction on Audio, Speech, and Language Processing, 2024. Architecture DCCTN network is four-fold. (1) propose a fully complex-valued deep complex convolution transformer ...
一定距离和雷达截面积条件下的探测要求,导致信(SpeechEnhancementTransformer,SETransformer) 号被淹没在噪声中,对非合作接收机后续检测信号网络,提出了一种基于多头注意力机制的语音增强 造成了极大困难[3]。因此侦察接收机无法获得预处技术,推理速度快于标准的长短期记忆(Long ...
Mohamed, “Learning audio-visual speech representation by masked multimodal cluster prediction,” arXiv, 2022. [179] K. Ramesh, C. Xing, W. Wang, D. Wang, and X. Chen, “Vset: A multimodal transformer for visual speech enhancement,” in ICASSP, 2021. [180] R. Zheng, J. Chen, ...
这个模型的网络结构和TSTNN: Two-stage transformer based neural network for speech enhancement in the time domain,ICASSP2021基本一模一样唯一的区别就是此文的输入是时频复值张量,TSTNN的是时域波形,我想知道这样只改输入提升了精度也可以发ICASSP吗 2023-06-30· 江苏 回复喜欢 想家又不想家 真的...