TITLE: DISCO-10M: A Large-Scale Music Dataset 

ABSTRACT: Music datasets play a crucial role in advancing research in machine learning
for music. However, existing music datasets suffer from limited size,
accessibility, and lack of audio resources. To address these shortcomings, we
present DISCO-10M, a novel and extensive music dataset that surpasses the
largest previously available music dataset by an order of magnitude. To ensure
high-quality data, we implement a multi-stage filtering process. This process
incorporates similarities based on textual descriptions and audio embeddings.
Moreover, we provide precomputed CLAP embeddings alongside DISCO-10M,
facilitating direct application on various downstream tasks. These embeddings
enable efficient exploration of machine learning applications on the provided
data. With DISCO-10M, we aim to democratize and facilitate new research to help
advance the development of novel machine learning models for music.
Music datasets play a crucial role in advancing research in machine learning
for music. However, existing music datasets suffer from limited size,
accessibility, and lack of audio resources. To address these shortcomings, we
present DISCO-10M, a novel and extensive music dataset that surpasses the
largest previously available music dataset by an order of magnitude. To ensure
high-quality data, we implement a multi-stage filtering process. This process
incorporates similarities based on textual descriptions and audio embeddings.
Moreover, we provide precomputed CLAP embeddings alongside DISCO-10M,
facilitating direct application on various downstream tasks. These embeddings
enable efficient exploration of machine learning applications on the provided
data. With DISCO-10M, we aim to democratize and facilitate new research to help
advance the development of novel machine learning models for music.
DISCO-10M: A Large-Scale Music Dataset
http://arxiv.org/abs/2306.13512v1