Adaptive Audio Classification Framework for in-Vehicle Environment with Dynamic Noise Characteristics

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Alsaadan, Haitham
South Dakota State University
Computer Sciences; Electrical and Computer Engineering
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With ever-increasing number of car-mounted electric devices that are accessed, managed, and controlled with smartphones, car apps are becoming an important part of the automotive industry. Audio classification is one of the key components of car apps as a front-end technology to enable human-app interactions. Existing approaches for audio classification, however, fall short as the unique and time-varying audio characteristics of car environments are not appropriately taken into account. Leveraging recent advances in mobile sensing technology that allows for an active and accurate driving environment detection, in this thesis, we develop an audio classification framework for mobile apps that categorizes an audio stream into music, speech, speech and music, and noise, adaptability depending on different driving environments. A case study is performed with four different driving environments, i.e., highway, local road, crowded city, and stopped vehicle. More than 420 minutes of audio data are collected including various genres of music, speech, speech and music, and noise from the driving environments.