MFF-EINV2: Multi-scale Feature Fusion across Spectral-Spatial-Temporal Domains for Sound Event Localization and Detection
<|reference_start|>MFF-EINV2: Multi-scale Feature Fusion across Spectral-Spatial-Temporal Domains for Sound Event Localization and Detection: Sound Event Localization and Detection (SELD) involves detecting and localizing sound events using multichannel sound recordings. Previously proposed Event-Independent Network V2 (EINV2) has achieved outstanding performance on SELD. However, it still faces challenges in effectively extracting features across spectral, spatial, and temporal domains. This paper proposes a three-stage network structure named Multi-scale Feature Fusion (MFF) module to fully extract multi-scale features across spectral, spatial, and temporal domains. The MFF module utilizes parallel subnetworks architecture to generate multi-scale spectral and spatial features. The TF-Convolution Module is employed to provide multi-scale temporal features. We incorporated MFF into EINV2 and term the proposed method as MFF-EINV2. Experimental results in 2022 and 2023 DCASE challenge task3 datasets show the effectiveness of our MFF-EINV2, which achieves state-of-the-art (SOTA) performance compared to published methods.<|reference_end|>
@article{mu2024mff-einv2:,
    title={MFF-EINV2: Multi-scale Feature Fusion across Spectral-Spatial-Temporal
  Domains for Sound Event Localization and Detection},
    author={Da Mu, Zhicheng Zhang, Haobo Yue},
    journal={arXiv preprint arXiv:2406.08771},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08771},
    primaryClass={cs.SD cs.AI eess.AS}
}