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Convention Paper

AlbumDB: A multitrack dataset of a ten-song album with stereo and immersive 7.1.4 masters

Authors: McKenzie, Thomas; Moliner, Eloi; Wright, Alec

Convention Paper · Paper 10278 · May 2026

Abstract

This paper presents a multitrack dataset intended to support research and education in music production. The dataset comprises a cohesive 10-song indie album (indie rock/folk) with separate stems for individual instruments, totalling between 13 and 35 individual stems per song. For each song, stems are provided in three variants: raw unprocessed stems, mixed stems but without reverberation or delay effects, and fully mixed stems. Additionally, two master formats are provided: stereo and immersive 7.1.4. This album-format dataset enables studies on mix
consistency across a thematically aligned collection of songs, as well as stereo upmixing to immersive formats, and contains many more stems per song than traditional four-stem datasets. Finally, to illustrate an example research usage of the dataset, the MEGAMI automatic mixing model is used to mix two songs. The results show that many of the MEGAMI mixing decisions are similar to those of the human mixes. The dataset is made open-access and free to download.

Details

AES Convention
160
Paper number
10278
Publication date
May 28, 2026
Session subject
AI and Machine Learning in Audio, Recording, Production, and Reproduction
Affiliation
Acoustics and Audio Group, Reid School of Music, University of Edinburgh; Acoustics and Audio Group, Reid School of Music, University of Edinburgh; Acoustics Lab, Department of Information and Communications Engineering, Aalto University (See document for exact affiliation information.)
Type
Convention Paper