论文标题
Syncnet：使用因果汇合并将目标关联以进行音频信号的时间延迟估计
SyncNet: Using Causal Convolutions and Correlating Objective for Time Delay Estimation in Audio Signals
论文作者
论文摘要
本文介绍了在嘈杂和回荡环境中进行音频信号中强大可靠的时间估计的任务。与流行的基于信号处理的方法相反，本文提出了基于机器学习的方法，即，由一组基于相关的目标函数的因果关系和抗毒物层组成的半杂音卷积神经网络。网络中的因果关系可确保未来时间间隔内的表示形式和所提出的损失函数的无孔，使网络在实际时间延迟时生成具有高相关性的序列。提出的方法也可以在本质上解释，因为它不会丢失时间信息。即使是浅卷积网络也能够以序列捕获本地模式，同时也可以将它们与全球关联。 Syncnet在估计不同类型的音频信号（包括脉搏，语音和音乐节拍）的相互时间延迟方面优于其他经典方法。
This paper addresses the task of performing robust and reliable time-delay estimation in audio-signals in noisy and reverberating environments. In contrast to the popular signal processing based methods, this paper proposes machine learning based method, i.e., a semi-causal convolutional neural network consisting of a set of causal and anti-causal layers with a novel correlation-based objective function. The causality in the network ensures non-leakage of representations from future time-intervals and the proposed loss function makes the network generate sequences with high correlation at the actual time delay. The proposed approach is also intrinsically interpretable as it does not lose time information. Even a shallow convolution network is able to capture local patterns in sequences, while also correlating them globally. SyncNet outperforms other classical approaches in estimating mutual time delays for different types of audio signals including pulse, speech and musical beats.
