Music Classification
20 papers with code • 0 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Music Classification
Libraries
Use these libraries to find Music Classification models and implementationsDatasets
Latest papers
Combinatorial music generation model with song structure graph analysis
In this work, we propose a symbolic music generation model with the song structure graph analysis network.
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.
MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models
For developers and amateurs, it is very difficult to grasp all of these task to satisfy their requirements in music processing, especially considering the huge differences in the representations of music data and the model applicability across platforms among various tasks.
Audio Embeddings as Teachers for Music Classification
Music classification has been one of the most popular tasks in the field of music information retrieval.
CLaMP: Contrastive Language-Music Pre-training for Cross-Modal Symbolic Music Information Retrieval
We introduce CLaMP: Contrastive Language-Music Pre-training, which learns cross-modal representations between natural language and symbolic music using a music encoder and a text encoder trained jointly with a contrastive loss.
Symbolic Music Structure Analysis with Graph Representations and Changepoint Detection Methods
In the past, there have been several works that attempt to segment music into the audio and symbolic domains, however, the identification and segmentation of the music structure at different levels is still an open research problem in this area.
Toward Universal Text-to-Music Retrieval
This paper introduces effective design choices for text-to-music retrieval systems.
Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming
In this work, we introduce a novel method for leveraging pre-trained models for low-resource (music) classification based on the concept of Neural Model Reprogramming (NMR).
S3T: Self-Supervised Pre-training with Swin Transformer for Music Classification
To our knowledge, S3T is the first method combining the Swin Transformer with a self-supervised learning method for music classification.
Music Classification: Beyond Supervised Learning, Towards Real-world Applications
The target audience for this web book is researchers and practitioners who are interested in state-of-the-art music classification research and building real-world applications.