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Sound Source Localization Using a Convolutional Neural Network and Regression Model.
Tan-Hsu Tan1, Yu-Tang Lin1, Yang-Lang Chang1
1Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Sensors (Basel, Switzerland)
|December 10, 2021
Summary
This study introduces a novel Convolutional Neural Network-Regression (CNN-R) model for precise sound source localization. The CNN-R model accurately estimates sound source angle and distance using interaural phase difference features.
Area of Science:
- Acoustics and Signal Processing
- Machine Learning for Audio Analysis
Background:
- Accurate sound source localization is crucial for applications like robotics and augmented reality.
- Existing methods often struggle with accuracy in complex acoustic environments.
Purpose of the Study:
- To develop and evaluate a novel Convolutional Neural Network-Regression (CNN-R) model for sound source localization.
- To estimate both the angle and distance of a sound source using acoustic features.
Main Methods:
- Extraction of interaural phase difference (IPD) features from the time-frequency domain using short-time Fourier transform (STFT).
- Utilizing a CNN-R model, treating IPD feature maps as images for localization.
- Dataset generation using Pyroomacoustics and the MIRD database for simulated and real-world impulse responses.
Main Results:
- Achieved high average accuracies: 98.96% for angle and 98.31% for distance in simulations (SNR=30 dB, RT60=0.16 s).
- Demonstrated superior performance in real environments with average accuracies of 99.85% for angle and 99.38% for distance.
- Outperformed existing sound source localization models in both simulated and real scenarios.
Conclusions:
- The proposed CNN-R model offers a significant advancement in sound source localization accuracy.
- The model's high performance indicates strong potential for practical, real-life applications.
- This approach provides a robust solution for determining sound source position based on acoustic cues.
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