CNN Architectures for Large-Scale Audio Classification

Autor: Hershey, Shawn, Chaudhuri, Sourish, Ellis, Daniel P. W., Gemmeke, Jort F., Jansen, Aren, Moore, R. Channing, Plakal, Manoj, Platt, Devin, Saurous, Rif A., Seybold, Bryan, Slaney, Malcolm, Weiss, Ron J., Wilson, Kevin

Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 vide

Externí odkaz: http://arxiv.org/abs/1609.09430

Content-Based Music Recommendation with the LFM-1b Dataset and Sample-Level Deep Convolutional Neural Networks

Autor: Platt, Devin

Publikováno v: Platt, Devin. (2017). Content-Based Music Recommendation with the LFM-1b Dataset and Sample-Level Deep Convolutional Neural Networks. UC San Diego: Computer Science. Retrieved from: http://www.escholarship.org/uc/item/3137p3v0

Content-based music classification systems attempt to predict musical attributes of songs directly from their audio content. Commonly, the ground truth labels are explicitly identified traits such as the genre of a piece of music. Ground truth annota

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______325::f1a6c0003d4fbb461464031c66ac7020
http://www.escholarship.org/uc/item/3137p3v0

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