What Makes Sound Event Localization and Detection Difficult? Insights from Error Analysis

• URL: http://arxiv.org/abs/2107.10469v1
• Date: Thu, 22 Jul 2021 06:01:49 GMT
• Title: What Makes Sound Event Localization and Detection Difficult? Insights from Error Analysis
• Authors: Thi Ngoc Tho Nguyen and Karn N. Watcharasupat and Zhen Jian Lee and Ngoc Khanh Nguyen and Douglas L. Jones and Woon Seng Gan
• Abstract summary: Sound event localization and detection (SELD) is an emerging research topic that aims to unify the tasks of sound event detection and direction-of-arrival estimation. SELD inherits the challenges of both tasks, such as noise, reverberation, interference, polyphony, and non-stationarity of sound sources. Previous studies have shown that unknown interferences in reverberant environments often cause major degradation in the performance of SELD systems.
• Score: 15.088901748728391
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sound event localization and detection (SELD) is an emerging research topic that aims to unify the tasks of sound event detection and direction-of-arrival estimation. As a result, SELD inherits the challenges of both tasks, such as noise, reverberation, interference, polyphony, and non-stationarity of sound sources. Furthermore, SELD often faces an additional challenge of assigning correct correspondences between the detected sound classes and directions of arrival to multiple overlapping sound events. Previous studies have shown that unknown interferences in reverberant environments often cause major degradation in the performance of SELD systems. To further understand the challenges of the SELD task, we performed a detailed error analysis on two of our SELD systems, which both ranked second in the team category of DCASE SELD Challenge, one in 2020 and one in 2021. Experimental results indicate polyphony as the main challenge in SELD, due to the difficulty in detecting all sound events of interest. In addition, the SELD systems tend to make fewer errors for the polyphonic scenario that is dominant in the training set.

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