@inproceedings{0634c9df96ba4120bf41341113dfec3a,
title = "Sound Event Localization and Detection Based on Time-Frequency Separable Convolutional Compression Network",
abstract = "This work proposes a Time-Frequency Separable Convolutional Compression Network (TFSCCN) as a system architecture for sound event localization and detection. It utilizes 1-D convolution kernels of different dimensions to extract features of time and frequency components separately, and also reduces the amount of model parameters by controlling the increase or decrease of the number of channels in the neural network. In addition, the model combines multi-head self-attention (MHSA) to obtain global and local information in time series features, and uses dual-branch tracking technology to effectively locate and detect the same or different overlapping sound events.",
keywords = "dual-branch tracking, multi-head self-attention, sound event localization and detection, time-frequency separable convolutional compression network",
author = "Yang, {Shih Tsung} and Jhou, {Fong Ci} and Wang, {Jia Ching} and Chang, {Pao Chi}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 10th IEEE Global Conference on Consumer Electronics, GCCE 2021 ; Conference date: 12-10-2021 Through 15-10-2021",
year = "2021",
doi = "10.1109/GCCE53005.2021.9622019",
language = "???core.languages.en_GB???",
series = "2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "432--433",
booktitle = "2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021",
}