The Spheres Dataset: Multitrack Orchestral Recordings for Music Source Separation and Information Retrieval

TL;DR

The Spheres dataset provides multitrack orchestral recordings with detailed acoustic data, designed to advance machine learning research in music source separation and related MIR tasks in classical music.

Contribution

It introduces a comprehensive orchestral dataset with multi-microphone recordings, isolated stems, and room impulse responses for benchmarking and developing separation algorithms.

Findings

Baseline models show promising separation performance.

The dataset reveals challenges in complex orchestral source separation.

Acoustic analysis offers insights into recording space characteristics.

Abstract

This paper introduces The Spheres dataset, multitrack orchestral recordings designed to advance machine learning research in music source separation and related MIR tasks within the classical music domain. The dataset is composed of over one hour recordings of musical pieces performed by the Colibr\`i Ensemble at The Spheres recording studio, capturing two canonical works - Tchaikovsky's Romeo and Juliet and Mozart's Symphony No. 40 - along with chromatic scales and solo excerpts for each instrument. The recording setup employed 23 microphones, including close spot, main, and ambient microphones, enabling the creation of realistic stereo mixes with controlled bleeding and providing isolated stems for supervised training of source separation models. In addition, room impulse responses were estimated for each instrument position, offering valuable acoustic characterization of the recording space. We present the dataset structure, acoustic analysis, and baseline evaluations using X-UMX based models for orchestral family separation and microphone debleeding. Results highlight both the potential and the challenges of source separation in complex orchestral scenarios, underscoring the dataset's value for benchmarking and for exploring new approaches to separation, localization, dereverberation, and immersive rendering of classical music.