{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sample-level-deep-convolutional-neural","title":"Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms","arxiv_id":"1703.01789","date":"2017-03-06","proceeding":null,"authors":["Jongpil Lee","Jiyoung Park","Keunhyoung Luke Kim","Juhan Nam"],"abstract":"Recently, the end-to-end approach that learns hierarchical representations\nfrom raw data using deep convolutional neural networks has been successfully\nexplored in the image, text and speech domains. This approach was applied to\nmusical signals as well but has been not fully explored yet. To this end, we\npropose sample-level deep convolutional neural networks which learn\nrepresentations from very small grains of waveforms (e.g. 2 or 3 samples)\nbeyond typical frame-level input representations. Our experiments show how deep\narchitectures with sample-level filters improve the accuracy in music\nauto-tagging and they provide results comparable to previous state-of-the-art\nperformances for the Magnatagatune dataset and Million Song Dataset. In\naddition, we visualize filters learned in a sample-level DCNN in each layer to\nidentify hierarchically learned features and show that they are sensitive to\nlog-scaled frequency along layer, such as mel-frequency spectrogram that is\nwidely used in music classification systems.","url_abs":"http://arxiv.org/abs/1703.01789v2","url_pdf":"http://arxiv.org/pdf/1703.01789v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sample-level-deep-convolutional-neural","repo_url":"https://github.com/jongpillee/music_dataset_split","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"sample-level-deep-convolutional-neural","repo_url":"https://github.com/kyungyunlee/sampleCNN-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"sample-level-deep-convolutional-neural","repo_url":"https://github.com/tae-jun/sample-cnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"music-auto-tagging","task_name":"Music Auto-Tagging"},{"task_slug":"music-classification","task_name":"Music Classification"}],"methods":[{"method_slug":"dcnn","method_name":"DCNN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.01789","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.01789"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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