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Main Authors: Shen, Yu-Han, He, Ke-Xin, Zhang, Wei-Qiang
Format: Preprint
Published: 2018
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Online Access:https://arxiv.org/abs/1810.11939
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author Shen, Yu-Han
He, Ke-Xin
Zhang, Wei-Qiang
author_facet Shen, Yu-Han
He, Ke-Xin
Zhang, Wei-Qiang
contents In this paper, we propose a temporal-frequential attention model for sound event detection (SED). Our network learns how to listen with two attention models: a temporal attention model and a frequential attention model. Proposed system learns when to listen using the temporal attention model while it learns where to listen on the frequency axis using the frequential attention model. With these two models, we attempt to make our system pay more attention to important frames or segments and important frequency components for sound event detection. Our proposed method is demonstrated on the task 2 of Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 Challenge and achieves competitive performance.
format Preprint
id arxiv_https___arxiv_org_abs_1810_11939
institution arXiv
publishDate 2018
record_format arxiv
spellingShingle Learning How to Listen: A Temporal-Frequential Attention Model for Sound Event Detection
Shen, Yu-Han
He, Ke-Xin
Zhang, Wei-Qiang
Sound
Audio and Speech Processing
In this paper, we propose a temporal-frequential attention model for sound event detection (SED). Our network learns how to listen with two attention models: a temporal attention model and a frequential attention model. Proposed system learns when to listen using the temporal attention model while it learns where to listen on the frequency axis using the frequential attention model. With these two models, we attempt to make our system pay more attention to important frames or segments and important frequency components for sound event detection. Our proposed method is demonstrated on the task 2 of Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 Challenge and achieves competitive performance.
title Learning How to Listen: A Temporal-Frequential Attention Model for Sound Event Detection
topic Sound
Audio and Speech Processing
url https://arxiv.org/abs/1810.11939