7 papers with code • 3 benchmarks • 3 datasets
Lastly, we show that self-supervised pre-training allows us to learn efficiently on smaller labeled datasets: we still achieve a score of 33. 1% despite using only 259 labeled songs during fine-tuning.
Ranked #1 on Music Auto-Tagging on MagnaTagATune (ROC AUC metric)
Recent work has shown that the end-to-end approach using convolutional neural network (CNN) is effective in various types of machine learning tasks.
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.
Recently deep learning based recommendation systems have been actively explored to solve the cold-start problem using a hybrid approach.
Second, we extract audio features from each layer of the pre-trained convolutional networks separately and aggregate them altogether given a long audio clip.
The goal of this paper to generate a visually appealing video that responds to music with a neural network so that each frame of the video reflects the musical characteristics of the corresponding audio clip.
Ranked #1 on Music Auto-Tagging on TimeTravel (using extra training data)