Methods and Datasets for DJ-Mix Reverse Engineering - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

Methods and Datasets for DJ-Mix Reverse Engineering


DJ techniques are an important part of popular music culture. However, they are also not sufficiently investigated by researchers due to the lack of annotated datasets of DJ mixes. Thus, this paper aims at filling this gap by introducing novel methods to automatically deconstruct and annotate recorded mixes for which the constituent tracks are known. A rough alignment first estimates where in the mix each track starts, and which time-stretching factor was applied. Second, a sample-precise alignment is applied to determine the exact offset of each track in the mix. Third, we propose a new method to estimate the cue points and the fade curves which operates in the time-frequency domain to increase its robustness to interference with other tracks. The proposed methods are finally evaluated on our new publicly available DJ-mix dataset. This dataset contains automatically generated beat-synchronous mixes based on freely available music tracks, and the ground truth about the placement of tracks in a mix.
Fichier principal
Vignette du fichier
schwarz-fourer-cmmr2019-unmixing.pdf (1.25 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-02172427 , version 1 (03-07-2019)


  • HAL Id : hal-02172427 , version 1


Diemo Schwarz, Dominique Fourer. Methods and Datasets for DJ-Mix Reverse Engineering. 14th International Symposium on Computer Music Multidisciplinary Research (CMMR 2019), Oct 2019, Marseille, France. ⟨hal-02172427⟩
176 View
326 Download


Gmail Facebook X LinkedIn More