Adaptive Speech Enhancement and Signal Separation Using Robust Neural Network Estimation Techniques

该项目的目标是开发鲁棒的方法来增强现实世界嘈杂环境中的语音。提出了一种用于时域增强的神经网络方法。与之前的神经网络方法不同，该方法只假设噪声信号的可用性。有效地，在特定的有噪声语音信号上训练一系列神经网络，从而产生一个非平稳模型，该模型可用于从给定信号中去除噪声。开发了两个相关框架：(1)基于卡尔曼滤波理论非线性扩展的神经状态空间方法；(2)从噪声语音到增强语音的直接神经映射。这项研究代表了需要稳健语音通信的交互系统的基本使能技术。更传统的方法通常在理想情况下是有效的，但对于现实世界的非平稳噪声源往往会下降。

The objective of this project is the development of robust methods for enhancing speech in real-world noisy environments. A neural network approach is developed for time-domain enhancement. The availability of only the noisy signal is assumed, in contrast to previous neural network approaches. Effectively, a sequence of neural networks is trained on the specific noisy speech signal, resulting in a nonstationary model that can be used to remove noise from the given signal. Two related frameworks are developed: (1) a neural state-space approach based on nonlinear extensions to Kalman filter theory and (2) a direct neural mapping from noisy speech to enhanced speech. This research represents fundamental enabling technology for interactive systems requiring robust speech communications. More traditional methods are often effective under ideal situations, but tend to degrade for real world nonstationary noise sources.