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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主要な著者:	LiFeng Liu, QiNan Xu, ShaoHua Mao, JianKang Mu, XuXia Zhao, WeiHua Song, Ping Cheng
フォーマット:	Artigo
言語:	Inglês
出版事項:	SpringerOpen 2025-09-01
シリーズ:	EURASIP Journal on Wireless Communications and Networking
主題:	Noise source classification Transfer learning YAMNet MFCC STFT Communications signal processing
オンライン･アクセス:	https://doi.org/10.1186/s13638-025-02483-8
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