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 pa...

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Autori principali:	Robin Inho Kee, Dahyun Nam, Seok-Jun Buu, Sung-Bae Cho
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	IEEE 2024-01-01
Serie:	IEEE Access
Soggetti:	Acoustic classification representation learning few-shot learning (FSL) in-vehicle noise prototypical network triplet loss
Accesso online:	https://ieeexplore.ieee.org/document/10521661/
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