Music Auto-Tagging
12 papers with code • 4 benchmarks • 3 datasets
Most implemented papers
Evaluation of CNN-based Automatic Music Tagging Models
Recent advances in deep learning accelerated the development of content-based automatic music tagging systems.
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.
Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms
Recent work has shown that the end-to-end approach using convolutional neural network (CNN) is effective in various types of machine learning tasks.
Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features
Along with the evolution of music technology, a large number of styles, or "subgenres," of Electronic Dance Music(EDM) have emerged in recent years.
Pre-training Music Classification Models via Music Source Separation
In this paper, we study whether music source separation can be used as a pre-training strategy for music representation learning, targeted at music classification tasks.
A Deep Bag-of-Features Model for Music Auto-Tagging
Feature learning and deep learning have drawn great attention in recent years as a way of transforming input data into more effective representations using learning algorithms.
Multi-Level and Multi-Scale Feature Aggregation Using Pre-trained Convolutional Neural Networks for Music Auto-tagging
Second, we extract audio features from each layer of the pre-trained convolutional networks separately and aggregate them altogether given a long audio clip.
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.
TräumerAI: Dreaming Music with StyleGAN
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.
Contrastive Learning of Musical Representations
A linear classifier trained on the proposed representations achieves a higher average precision than supervised models on the MagnaTagATune dataset, and performs comparably on the Million Song dataset.