# Adaptive Multi-Class Audio Classification in Noisy In-Vehicle Environment

## TL;DR

This paper presents an adaptive multi-class audio classification system tailored for noisy in-vehicle environments, significantly improving accuracy by accounting for dynamic driving conditions.

## Contribution

It introduces a novel adaptive audio classification approach that considers different driving environments, enhancing accuracy over traditional non-adaptive methods.

## Key findings

- Achieved up to 166% improvement in overall classification accuracy.
- Improved accuracy by 64% for speech and speech+music classes.
- Collected over 420 minutes of diverse in-vehicle audio data.

## Abstract

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.