Ambulance Siren Detection for Autonomous Cars using Machine Learning

Client Overview

US-based technology company providing next-generation solutions for automotive industry.

Business Challenge

The client wanted to develop a feature for autonomous cars that will intelligently identity ambulance/fire trucks/police siren and will give way to it. For the same, client required a development partner with expertise in ML in automotive sector who can help them develop the functionality accelerating their launch timelines. 

VOLANSYS Contribution
  • Signal Processing Algorithms
    • Designed Band Pass Filter to remove unwanted frequencies
    • MFCCs computation to extract the audio features
    • Fourier Decomposition to apply statistics on sub bands
  • Machine Learning – Deep Learning
    • Designed the solution using supervised learning algorithm–Artificial Neural Network (ANN)
    • Developed model using TensorFlow framework and trained model with extracted features from 9000 audio files
    • Trained model has 90% prediction accuracy
    • Developed application on NXP i.MXRT series for extracting the features from real audio samples, pre-processing and running inference for ambulance siren detection
  • Quality Engineering
    • Performance evaluation using real ambulance siren through microphone
Technologies | Engineering Expertise

Machine Learning | Deep Learning | Quality Engineering | TensorFlow | Signal Processing

Application Diagram

Ambulance siren detection diagram

Benefits Delivered
  • Improved on-road safety with audio detection feature implementation
  • Helped upsell end-customer’s product by enhancing the autonomous car feature set
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