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

arXiv: https://arxiv.org/abs/2511.21247 · HTML: https://mlsanity.com/paper/2511.21247/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 49 references that are in the map.
- First posted on arXiv 26 Nov 2025.

## Builds on (most cited first, 21 of 21)

- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Hybrid Transformers for Music Source Separation](https://mlsanity.com/paper/2211.08553/) (2022)
- [Music Source Separation in the Waveform Domain](https://mlsanity.com/paper/1911.13254/) (2019)
- [Hybrid Spectrogram and Waveform Source Separation](https://mlsanity.com/paper/2111.03600/) (2021)
- [Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity](https://mlsanity.com/paper/1909.08494/) (2019)
- [Music Source Separation with Band-split RNN](https://mlsanity.com/paper/2209.15174/) (2022)
- [Creating A Multi-track Classical Musical Performance Dataset for Multimodal Music Analysis: Challenges, Insights, and Applications](https://mlsanity.com/paper/1612.08727/) (2016)
- [Asteroid: the PyTorch-based audio source separation toolkit for researchers](https://mlsanity.com/paper/2005.04132/) (2020)
- [Music Demixing Challenge 2021](https://mlsanity.com/paper/2108.13559/) (2021)
- [Music Source Separation with Band-Split RoPE Transformer](https://mlsanity.com/paper/2309.02612/) (2023)
- [All for One and One for All: Improving Music Separation by Bridging Networks](https://mlsanity.com/paper/2010.04228/) (2020)
- [The Sound Demixing Challenge 2023 unicodex2013 Music Demixing Track](https://mlsanity.com/paper/2308.06979/) (2023)
- [Conditioned Source Separation for Music Instrument Performances](https://mlsanity.com/paper/2004.03873/) (2020)
- [Objective Measures of Perceptual Audio Quality Reviewed: An Evaluation of Their Application Domain Dependence](https://mlsanity.com/paper/2110.11438/) (2021)
- [Music Source Separation Based on a Lightweight Deep Learning Framework (DTTNET: DUAL-PATH TFC-TDF UNET)](https://mlsanity.com/paper/2309.08684/) (2023)
- [SCNet: Sparse Compression Network for Music Source Separation](https://mlsanity.com/paper/2401.13276/) (2024)
- [Why does music source separation benefit from cacophony?](https://mlsanity.com/paper/2402.18407/) (2024)
- [Mixing-Specific Data Augmentation Techniques for Improved Blind Violin/Piano Source Separation](https://mlsanity.com/paper/2008.02480/) (2020)
- [SynthSOD: Developing an Heterogeneous Dataset for Orchestra Music Source Separation](https://mlsanity.com/paper/2409.10995/) (2024)
- [A study of audio mixing methods for piano transcription in violin-piano ensembles](https://mlsanity.com/paper/2305.13758/) (2023)
- [Score-informed Music Source Separation: Improving Synthetic-to-real Generalization in Classical Music](https://mlsanity.com/paper/2503.07352/) (2025)

## Built on by (most cited first, 1 of 1)

- [End-to-End Historical Music Restoration in Latent Space](https://mlsanity.com/paper/2610.00607/) (2026)

## Abstract (arXiv)

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.