• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Asilomar Conference on Signals, Systems, and Computers >A new feature set for masking-based monaural speech separation
【24h】

A new feature set for masking-based monaural speech separation

机译：用于基于掩蔽的单声道语音分离的新功能

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We propose a new feature based on a gammatone filter bank for improving monaural speech separation using neural networks. This new feature encodes not only the local information of cochleagram, and spectrotemporal context, similar to previous approaches, but also captures time-frequency dynamics in the spectrotemporal context using an image processing technique. Speech separation was achieved by computing optimal time-frequency masks using two types of neural networks (DNN and LSTM) to determine the interactions between feature and training model properties. The performance of our feature was evaluated in a variety of simulated environments having different non-stationary noises and reverberation times and quantified using three objective measures. Experimental results show that the proposed monaural feature set improves the objective speech intelligibility, speech quality and signal-to-noise ratio compared to prior feature sets in noisy and reverberant environments with particular benefit in speech intelligibility.
机译：我们提出了一种基于伽马托滤波器的新功能，用于使用神经网络改善单声道语音分离。该新功能不仅可以编码Cochleagram的本地信息，以及类似于先前的方法，而且使用图像处理技术捕获光谱仪器上下文中的时间频率动态。通过使用两种类型的神经网络（DNN和LSTM）计算最佳时频掩模来实现语音分离，以确定特征和训练模型属性之间的交互。我们特征的性能被评估在各种模拟环境中，具有不同的非平稳噪声和混响时间，并使用三个客观措施量化。实验结果表明，与嘈杂和混响环境中的先前特征集相比，所提出的单声道特征组可提高目标语音可懂度，语音质量和信噪比，特别是语音可懂度特别益处。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号