Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization System
Vehicle-mounted sound source localization systems provide comprehensive information to improve driving conditions by monitoring the surroundings. The three-dimensional structure of vehicles hinders the omnidirectional sound localization system because of the long and uneven propagation. In the recei...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/3/925 |
| _version_ | 1811187134792466432 |
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| author | Yeonseok Park Anthony Choi Keonwook Kim |
| author_facet | Yeonseok Park Anthony Choi Keonwook Kim |
| author_sort | Yeonseok Park |
| collection | DOAJ |
| description | Vehicle-mounted sound source localization systems provide comprehensive information to improve driving conditions by monitoring the surroundings. The three-dimensional structure of vehicles hinders the omnidirectional sound localization system because of the long and uneven propagation. In the received signal, the flight times between microphones delivers the essential information to locate the sound source. This paper proposes a novel method to design a sound localization system based on the single analog microphone network. This article involves the flight time estimation for two microphones with non-parametric homomorphic deconvolution. The parametric methods are also suggested with Yule-walker, Prony, and Steiglitz-McBride algorithm to derive the coefficient values of the propagation model for flight time estimation. The non-parametric and Steiglitz-McBride method demonstrated significantly low bias and variance for 20 or higher ensemble average length. The Yule-walker and Prony algorithms showed gradually improved statistical performance for increased ensemble average length. Hence, the non-parametric and parametric homomorphic deconvolution well represent the flight time information. The derived non-parametric and parametric output with distinct length will serve as the featured information for a complete localization system based on machine learning or deep learning in future works. |
| first_indexed | 2024-04-11T13:57:57Z |
| format | Article |
| id | doaj.art-6da9cbb3517c4bd69fa810211f8e4e29 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:57:57Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6da9cbb3517c4bd69fa810211f8e4e292022-12-22T04:20:12ZengMDPI AGSensors1424-82202020-02-0120392510.3390/s20030925s20030925Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization SystemYeonseok Park0Anthony Choi1Keonwook Kim2Division of Electronics & Electrical Engineering, Dongguk University-Seoul, Seoul 04620, KoreaDepartment of Electrical & Computer Engineering, Mercer University, 1501 Mercer University Drive, Macon, GA 31207, USADivision of Electronics & Electrical Engineering, Dongguk University-Seoul, Seoul 04620, KoreaVehicle-mounted sound source localization systems provide comprehensive information to improve driving conditions by monitoring the surroundings. The three-dimensional structure of vehicles hinders the omnidirectional sound localization system because of the long and uneven propagation. In the received signal, the flight times between microphones delivers the essential information to locate the sound source. This paper proposes a novel method to design a sound localization system based on the single analog microphone network. This article involves the flight time estimation for two microphones with non-parametric homomorphic deconvolution. The parametric methods are also suggested with Yule-walker, Prony, and Steiglitz-McBride algorithm to derive the coefficient values of the propagation model for flight time estimation. The non-parametric and Steiglitz-McBride method demonstrated significantly low bias and variance for 20 or higher ensemble average length. The Yule-walker and Prony algorithms showed gradually improved statistical performance for increased ensemble average length. Hence, the non-parametric and parametric homomorphic deconvolution well represent the flight time information. The derived non-parametric and parametric output with distinct length will serve as the featured information for a complete localization system based on machine learning or deep learning in future works.https://www.mdpi.com/1424-8220/20/3/925sound source localizationtime of flighthomomorphic deconvolutioncepstrumyule-walkerpronysteiglitz-mcbridevehicle |
| spellingShingle | Yeonseok Park Anthony Choi Keonwook Kim Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization System Sensors sound source localization time of flight homomorphic deconvolution cepstrum yule-walker prony steiglitz-mcbride vehicle |
| title | Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization System |
| title_full | Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization System |
| title_fullStr | Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization System |
| title_full_unstemmed | Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization System |
| title_short | Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization System |
| title_sort | parametric estimations based on homomorphic deconvolution for time of flight in sound source localization system |
| topic | sound source localization time of flight homomorphic deconvolution cepstrum yule-walker prony steiglitz-mcbride vehicle |
| url | https://www.mdpi.com/1424-8220/20/3/925 |
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