Adaptive Multi-Class Audio Classification in Noisy In-Vehicle Environment
This addresses the problem of accurate audio classification for automotive human-device interactions, though it is incremental as it adapts existing methods to a specific noisy environment.
The paper tackles audio classification in noisy in-vehicle environments by developing an adaptive system that classifies audio streams into categories like music and speech, achieving improvements of up to 166% in accuracy for speech classification compared to non-adaptive methods.
With ever-increasing number of car-mounted electric devices and their complexity, audio classification is increasingly important for the automotive industry as a fundamental tool for human-device interactions. Existing approaches for audio classification, however, fall short as the unique and dynamic audio characteristics of in-vehicle environments are not appropriately taken into account. In this paper, we develop an audio classification system that classifies an audio stream into music, speech, speech+music, and noise, adaptably depending on driving environments including highway, local road, crowded city, and stopped vehicle. More than 420 minutes of audio data including various genres of music, speech, speech+music, and noise are collected from diverse driving environments. The results demonstrate that the proposed approach improves the average classification accuracy up to 166%, and 64% for speech, and speech+music, respectively, compared with a non-adaptive approach in our experimental settings.