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Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization System
Yeonseok Park1, Anthony Choi2, Keonwook Kim1
1Division of Electronics & Electrical Engineering, Dongguk University-Seoul, Seoul 04620, Korea.
Sensors (Basel, Switzerland)
|February 14, 2020
Summary
This study introduces a novel sound localization system using a single analog microphone network. It accurately estimates flight times between microphones, crucial for vehicle-mounted sound source detection and future AI applications.
Area of Science:
- Engineering
- Acoustics
- Signal Processing
Background:
- Vehicle-mounted sound source localization systems aid driving by monitoring surroundings.
- Challenges exist in omnidirectional localization due to vehicle 3D structure and signal propagation complexities.
Purpose of the Study:
- To propose a novel sound localization system utilizing a single analog microphone network.
- To investigate flight time estimation methods for accurate sound source localization.
Main Methods:
- Flight time estimation using non-parametric homomorphic deconvolution for two microphones.
- Parametric methods including Yule-Walker, Prony, and Steiglitz-McBride algorithms for propagation model coefficient derivation.
Main Results:
- Non-parametric and Steiglitz-McBride methods showed low bias and variance with ensemble lengths of 20 or more.
- Yule-Walker and Prony algorithms demonstrated improved statistical performance with increased ensemble averaging.
- Both non-parametric and parametric homomorphic deconvolution effectively represent flight time information.
Conclusions:
- Homomorphic deconvolution methods, both non-parametric and parametric, are effective for flight time estimation in sound localization.
- The derived flight time information serves as key features for future machine learning and deep learning-based localization systems.
Keywords:
PronySteiglitz-McBrideYule-walkercepstrumhomomorphic deconvolutionsound source localizationtime of flightvehicleMore Related Videos
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