DISCO-10M: A Large-Scale Music Dataset

• URL: http://arxiv.org/abs/2306.13512v2
• Date: Thu, 5 Oct 2023 09:45:00 GMT
• Title: DISCO-10M: A Large-Scale Music Dataset
• Authors: Luca A. Lanzend\"orfer, Florian Gr\"otschla, Emil Funke, Roger Wattenhofer
• Abstract summary: We present DISCO-10M, a novel and extensive music dataset. It surpasses the largest previously available music dataset by an order of magnitude. We aim to democratize and facilitate new research to help advance the development of novel machine learning models for music.
• Score: 20.706469085872516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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