论文标题
动态声条件下的盲混响时间估计
Blind Reverberation Time Estimation in Dynamic Acoustic Conditions
论文作者
论文摘要
现实信号中回响时间的估计在广泛的应用中起着核心作用。在许多情况下，声学条件会随着时间的流逝而变化，这反过来又需要估计值不断更新。先前提出的涉及深神经网络的方法主要是在静态声学条件下设计和测试的。在这项工作中，我们表明这些方法在动态发展的声学环境中的性能差。在机器学习中以数据为中心的方法的最新趋势的推动下，我们提出了一种新颖的方式来生成培训数据，并使用现有的深层神经网络体系结构来证明，遵循回响时间的时间变化的能力有了显着改善。
The estimation of reverberation time from real-world signals plays a central role in a wide range of applications. In many scenarios, acoustic conditions change over time which in turn requires the estimate to be updated continuously. Previously proposed methods involving deep neural networks were mostly designed and tested under the assumption of static acoustic conditions. In this work, we show that these approaches can perform poorly in dynamically evolving acoustic environments. Motivated by a recent trend towards data-centric approaches in machine learning, we propose a novel way of generating training data and demonstrate, using an existing deep neural network architecture, the considerable improvement in the ability to follow temporal changes in reverberation time.
