US-based technology company providing next-generation solutions for automotive industry.
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.
- 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
Machine Learning | Deep Learning | Quality Engineering | TensorFlow | Signal Processing
- Improved on-road safety with audio detection feature implementation
- Helped upsell end-customer’s product by enhancing the autonomous car feature set