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Found 7 papers, 4 papers with code
Zero-shot Learning for Audio-based Music Classification and Tagging
Audio-based music classification and tagging is typically based on categorical supervised learning with a fixed set of labels.
Representation Learning of Music Using Artist, Album, and Track Information
Supervised music representation learning has been performed mainly using semantic labels such as music genres.
Zero-shot Learning and Knowledge Transfer in Music Classification and Tagging
Music classification and tagging is conducted through categorical supervised learning with a fixed set of labels.
Deep Content-User Embedding Model for Music Recommendation
Recently deep learning based recommendation systems have been actively explored to solve the cold-start problem using a hybrid approach.
Raw Waveform-based Audio Classification Using Sample-level CNN Architectures
Music, speech, and acoustic scene sound are often handled separately in the audio domain because of their different signal characteristics.
Representation Learning of Music Using Artist Labels
In this paper, we present a supervised feature learning approach using artist labels annotated in every single track as objective meta data.
Sound Audio and Speech Processing
Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms
Recently, the end-to-end approach that learns hierarchical representations from raw data using deep convolutional neural networks has been successfully explored in the image, text and speech domains.
