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arXiv · 2311.14068

Interactive Dual-Conformer with Scene-Inspired Mask for Soft Sound Event Detection

Han Yin·Jisheng Bai·Mou Wang·Dongyuan Shi·Woon-Seng Gan·Jianfeng Chen
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Abstract

Traditional binary hard labels for sound event detection (SED) lack details about the complexity and variability of sound event distributions. Recently, a novel annotation workflow is proposed to generate fine-grained non-binary soft labels, resulting in a new real-life dataset named MAESTRO Real for SED. In this paper, we first propose an interactive dual-conformer (IDC) module, in which a cross-interaction mechanism is applied to effectively exploit the information from soft labels. In addition, a novel scene-inspired mask (SIM) based on soft labels is incorporated for more precise SED predictions. The SIM is initially generated through a statistical approach, referred as SIM-V1. However, the fixed artificial mask may mismatch the SED model, resulting in limited effectiveness. Therefore, we further propose SIM-V2, which employs a word embedding model for adaptive SIM estimation. Experimental results show that the proposed IDC module can effectively utilize the information from soft labels, and the integration of SIM-V1 can further improve the accuracy. In addition, the impact of different word embedding dimensions on SIM-V2 is explored, and the results show that the appropriate dimension can enable SIM-V2 achieve superior performance than SIM-V1. In DCASE 2023 Challenge Task4B, the proposed system achieved the top ranking performance on the evaluation dataset of MAESTRO Real.

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BibTeXRIS

Han Yin, Jisheng Bai, Mou Wang, Dongyuan Shi, Woon-Seng Gan, Jianfeng Chen. 2023-11-23. Interactive Dual-Conformer with Scene-Inspired Mask for Soft Sound Event Detection. https://arxiv.org/abs/2311.14068

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