Machine learning based audio classification for fully automated validation of off highway infotainment systems

Modern infotainment systems generate a wide range of audio based signals such as indicator chimes, turn signals, infotainment system audio, navigation prompts, and warning alerts. This is a novel and modern approach towards accurately classifying and transcription of these audio to enable fully automated validation. The proposed solution employs a hybrid deep learning architecture leveraging convolutional neural networks (CNNs) and recurrent neural networks (RNNs) trained using labeled audio samples. Automatic Speech Recognition (ASR) models are integrated for transcribing spoken navigation prompts and commands from infotainment systems. The proposed system delivers reliable results in near real-time audio classification and transcription, facilitating error free automation and validation. This provides a robust foundation for edge deployable audio intelligence systems.