Summary Classification of ambulance siren sound with MFCC-SVM | AIP Conference Proceedings | AIP Publishing pubs.aip.org
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The article explores using the MFCC-SVM method to accurately classify ambulance siren sounds for monitoring.
Slides
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Key Points
- Ambulance siren sound classification using MFCC-SVM
- Application in traffic light system to monitor ambulance arrival
- Use of Mel-frequency cepstral coefficients-Support Vector Machine (MFCC-SVM)
- Data acquisition, feature extraction, algorithm exploration, and model deployment
- Importance of audio identification in pattern recognition and artificial intelligence
Summaries
29 word summary
This research article discusses the classification of ambulance siren sounds using the MFCC-SVM method. The goal is to accurately identify the sound of an ambulance siren for monitoring purposes.
76 word summary
This summary discusses a research article titled "Classification of ambulance siren sound with MFCC-SVM" published in the AIP Conference Proceedings. The article focuses on the classification of ambulance siren sounds using the MFCC-SVM method. The authors of
This paper discusses the classification of ambulance siren sounds using Mel-frequency cepstral coefficients-Support Vector Machine (MFCC-SVM). The goal is to develop a machine learning application that can accurately identify the sound of an ambulance siren in order to monitor