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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.13920 |
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| _version_ | 1866914845813637120 |
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| author | Pacheco-Gonzalez, Alberto Torres, Raymundo Chacon, Raul Robledo, Isidro |
| author_facet | Pacheco-Gonzalez, Alberto Torres, Raymundo Chacon, Raul Robledo, Isidro |
| contents | In emergency situations, the high-speed movement of an ambulance through the city streets can be hindered by vehicular traffic. This work presents a method for detecting emergency vehicle sirens in real time. To obtain the audio fingerprint of a Hi-Lo siren, DSP and signal symbolization techniques were applied, which were contrasted against an audio classifier based on a deep neural network, using the same 280 audios of ambient sounds and 52 Hi-Lo siren audios dataset. In both methods, some classification accuracy metrics were evaluated based on its confusion matrix, resulting in the DSP algorithm having a slightly lower accuracy than the DNN model, however, it offers a self-explanatory, adjustable, portable, high performance and lower energy and consumption that makes it a more viable lower cost ADAS implementation to identify Hi-Lo sirens in real time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_13920 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Real-Time Emergency Vehicle Detection using Mel Spectrograms and Regular Expressions Pacheco-Gonzalez, Alberto Torres, Raymundo Chacon, Raul Robledo, Isidro Sound Formal Languages and Automata Theory Symbolic Computation Audio and Speech Processing I.5.5 In emergency situations, the high-speed movement of an ambulance through the city streets can be hindered by vehicular traffic. This work presents a method for detecting emergency vehicle sirens in real time. To obtain the audio fingerprint of a Hi-Lo siren, DSP and signal symbolization techniques were applied, which were contrasted against an audio classifier based on a deep neural network, using the same 280 audios of ambient sounds and 52 Hi-Lo siren audios dataset. In both methods, some classification accuracy metrics were evaluated based on its confusion matrix, resulting in the DSP algorithm having a slightly lower accuracy than the DNN model, however, it offers a self-explanatory, adjustable, portable, high performance and lower energy and consumption that makes it a more viable lower cost ADAS implementation to identify Hi-Lo sirens in real time. |
| title | Real-Time Emergency Vehicle Detection using Mel Spectrograms and Regular Expressions |
| topic | Sound Formal Languages and Automata Theory Symbolic Computation Audio and Speech Processing I.5.5 |
| url | https://arxiv.org/abs/2309.13920 |