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Polyphonic sound event localization and detection based on Multiple Attention Fusion ResNet
Shouming Zhang1, Yaling Zhang1,2, Yixiao Liao2
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Mathematical Biosciences and Engineering : MBE
|March 8, 2024
Summary
This study introduces a novel Multiple Attention Fusion ResNet for accurate sound event localization and detection. The model effectively handles polyphony and noise, outperforming existing methods in complex acoustic environments.
Area of Science:
- Acoustics and Signal Processing
- Machine Learning for Audio Analysis
Background:
- Sound event localization and detection are crucial in diverse applications.
- Polyphony and noise interference pose significant challenges to accurate sound event prediction and localization.
Purpose of the Study:
- To develop an advanced deep learning model for robust sound event localization and detection.
- To address the challenges of polyphony and noise interference in complex acoustic environments.
Main Methods:
- A Multiple Attention Fusion ResNet architecture based on ResNet34 is proposed.
- Gated Channel Transform is integrated to enhance residual blocks, improving contextual information capture and noise reduction.
- Split Attention and Coordinate Attention mechanisms are employed to manage cross-channel information and focus on spatial sound event characteristics.
Main Results:
- The proposed model demonstrates significant performance improvements over state-of-the-art methods.
- Superior performance is achieved in environments with multiple polyphonic sounds and directional noise interference.
- Competitive results are obtained in scenarios with single polyphonic sound interference.
Conclusions:
- The Multiple Attention Fusion ResNet effectively enhances sound event localization and detection capabilities.
- The model's attention mechanisms are key to mitigating interference from polyphony and noise.
- This research offers a promising solution for real-world sound analysis challenges.
