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Although both CNNs and Transformers can achieve competitive performance, their necessity in specific scenarios remains debatable. To address this, we propose FPN-RepMLP, a Multi-Layer Perceptron (MLP) style neural network for music genre classification. Specifically, we enhance Fully Connected (FC) layers by incorporating local priors to strengthen music category recognition. During training, FPN-RepMLP internally constructs convolutional layers and merges them into FC layers for inference. Experiments on the GTZAN dataset demonstrate that a simple pure MLP achieves classification accuracy comparable to CNNs. Furthermore, by integrating FPN-RepMLP into traditional CNNs, we attain an accuracy of 91.36%, outperforming CNNs-based methods (e.g., AlexNet, GoogLeNet, ResNet) and Transformer-based algorithms by 2.63\u201313.57% points.<\/jats:p>","DOI":"10.1142\/s0218126625503542","type":"journal-article","created":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T04:52:58Z","timestamp":1747111978000},"source":"Crossref","is-referenced-by-count":0,"title":["Music Genre Classification Using Feature Pyramid Network and Reparameterized Multi-Layer Perceptron with Enhanced Local Prior"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4132-222X","authenticated-orcid":false,"given":"Lei","family":"Hua","sequence":"first","affiliation":[{"name":"Conservatory Music, Shangqiu Normal University, Shangqiu, Henan 476000, P. R. 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