Disentangled Prototypical Convolutional Network for Few-Shot Learning in In-Vehicle Noise Classification

This study addresses the persistent challenge of in-vehicle noise, a significant factor affecting customer satisfaction and safety in the automotive industry. Despite advancements in understanding various noise sources and mitigation strategies, vehicle noise continues to contribute to driver and passenger discomfort, impacting stress levels, fatigue, and overall quality of life. Recent research has made significant strides in classifying in-vehicle noise, yet the complexity of obtaining comprehensive and diverse datasets remains a major hurdle, given the variability and transient nature of these noises. To overcome these challenges, our research introduces an innovative approach using Few-shot Learning (FSL). We propose a unique FSL model that integrates a Triplet-trained Prototypical Network for the classification of in-vehicle noises. This model is particularly adept at learning robust feature representations from limited data. The application of triplet sampling and loss significantly enhances the model's ability to distinguish between various types of in-vehicle noises. Our methodology was rigorously tested using a specially curated dataset of in-vehicle noises, reflecting real-world diversity. The experimental results, obtained through 10-fold cross-validation, demonstrate an exceptional average accuracy of 96.81% on a 9-way 1-shot task. This level of accuracy, achieved with a limited amount of training data, not only attests to the effectiveness of our model but also marks a significant advancement in the field of acoustic classification. Our study's findings highlight the potential of FSL in addressing complex challenges in the automotive industry, paving the way for more effective noise reduction strategies and improved vehicle design.