Training audio transformers for cover song identification
Te Zeng,
Francis C. M. Lau
Abstract:In the past decades, convolutional neural networks (CNNs) have been commonly adopted in audio perception tasks, which aim to learn latent representations. However, for audio analysis, CNNs may exhibit limitations in effectively modeling temporal contextual information. Analogous to the successes of transformer architecture used in the fields of computer vision and audio classification, to capture long-range global contexts better, we here extend this line of work and propose an Audio Similarity Transformer (AS… Show more
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