implementation of "DCCRN-Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement" how to run torch==1.7 asteroid==0.3.4 change the "dns_home" of "conf.yml" to the dir of dns-datas -dns_datas/ -clean/ -noise/ -noisy/ run train.py on pycharm test score valid...
DCCRN:用于相位感知语音增强的深度复卷积递归网络 论文实现代码:huyanxin/DeepComplexCRN (github.com)maggie0830/DCCRN: implementation of "DCCRN-Deep Complex Convolution Recurrent Network for Phase-…
4 样例展示 请访问:https://imybo.github.io/S-DCCRN/ 5 参考文献 [1] Yanxin Hu, Yun Liu, Shubo Lv, Mengtao Xing, Shimin Zhang, Yihui Fu, Jian Wu, Bihong Zhang, and Lei Xie, "DCCRN:Deep complex convolution recurrent network for phase-awarespeech enhancement," Interspeech, pp. 2472–24...
样例:imybo.github.io/Spatial 图1 发表论文截图 1. 背景动机 语音增强(Speech Enhancement,SE)是一项从嘈杂语音中消除噪声,提高语音质量和可懂度的任务,是语音信号处理领域中的重要研究方向。最近,随着深度学习在语音应用上的飞速进展,语音增强被定义为一种数据驱动的监督学习问题。与此同时,基于深度学习的多通道语...
我們可以看到,我們提交的模型總體上表現良好。DCCRN-E實現了平均MOS 3.42在所有設定和4.00在無混響設定。我們的DCCRN-E的PyTorch實現(由ONNX匯出)的一幀處理時間是3.12毫秒,在Intel i5-8250U PC上進行了經驗測試。一些增強的音訊剪輯可以從https:// huyanxin.github.io/DeepComplexCRN找到。
implementation of "DCCRN-Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement" how to run numpy==1.19.0 librosa==0.8.0 torch==1.6.0 torchaudio-contrib==0.1 (git+https://github.com/keunwoochoi/torchaudio-contrib@61fc6a804c941dec3cf8a06478704d19fc5e415a) change the...