YAMNet-based transfer learning for compact noise classification in urban and wireless systems

Abstract Urban development and construction generate significant noise, which must be accurately monitored and classified to effectively mitigate its impact and ensure regulatory compliance. However, traditional noise recognition methods tend to perform poorly in complex, dynamic environments becaus...

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Bibliografiska uppgifter

Huvudupphov:	LiFeng Liu, QiNan Xu, ShaoHua Mao, JianKang Mu, XuXia Zhao, WeiHua Song, Ping Cheng
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	SpringerOpen 2025-09-01
Serie:	EURASIP Journal on Wireless Communications and Networking
Ämnen:	Noise source classification Transfer learning YAMNet MFCC STFT Communications signal processing
Länkar:	https://doi.org/10.1186/s13638-025-02483-8
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