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• W2952395326 abstract "Self-attention is an attention mechanism that learns a representation by relating different positions in the sequence. The transformer, which is a sequence model solely based on self-attention, and its variants achieved state-of-the-art results in many natural language processing tasks. Since music composes its semantics based on the relations between components in sparse positions, adopting the self-attention mechanism to solve music information retrieval (MIR) problems can be beneficial. Hence, we propose a self-attention based deep sequence model for music tagging. The proposed architecture consists of shallow convolutional layers followed by stacked Transformer encoders. Compared to conventional approaches using fully convolutional or recurrent neural networks, our model is more interpretable while reporting competitive results. We validate the performance of our model with the MagnaTagATune and the Million Song Dataset. In addition, we demonstrate the interpretability of the proposed architecture with a heat map visualization." @default.
• W2952395326 created "2019-06-27" @default.
• W2952395326 creator A5006479715 @default.
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• W2952395326 date "2019-06-12" @default.
• W2952395326 modified "2023-09-27" @default.
• W2952395326 title "Toward Interpretable Music Tagging with Self-Attention" @default.
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• W2952395326 doi "https://doi.org/10.48550/arxiv.1906.04972" @default.
• W2952395326 hasPublicationYear "2019" @default.
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