时间:   2018年6月8日（周五）上午9：30

地点:   校本部东区翔英大楼T516室

讲座:   基于深度学习的单通道音频分离系统研究进展

演讲者: 罗艺 博士 哥伦比亚大学

演讲者简介：

罗艺现为哥伦比亚大学的博士生。他的研究重点是音频前端处理中的机器学习以及深度学习系统，主要从事的工作有音频分离，语音增强，语音去混响等，同时还致力于构建BMIs（Brain-Machine Interfaces）以建立机器与生物系统之间的联系。

讲座摘要：

Single-channel audio separation has been an active research area for decades. Recently, deep learning-based systems have greatly advanced the state of this problem. In most of the deep learning systems, the separation is performed in time-frequency (T-F) domain where a T-F mask is estimated for each of the target source. However, the use of T-F masks upper-bounds the system’s performance and introduces difficulties in end-to-end separation. In this talk, I will first make an overview of several deep learning approaches in T-F domain, address their main disadvantages, and introduce the recently proposed Time-domain Audio Separation Network (TasNet) which separates the mixture directly in time-domain.

邀请人：通信与信息工程学院  朱梦尧副教授

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