Harmonic Mixing Based On Roughness And Pitch Commonality

This webpage contains sound examples to accompany the following paper presented at the DAFx-2015 conference:

R. Gebhardt, M. E. P. Davies and B. Seeber. Harmonic Mixing Based on Roughness and Pitch Commonality. In Proc. of the 18th Int. Conference on Digital Audio Effects (DAFx-15), Trondheim, Norway, Nov 30 - Dec 3, 2015. Winner of the Best Paper Award.

Abstract

The practice of harmonic mixing is a technique used by DJs for the beat-synchronous and harmonic alignment of two or more pieces of music. In this paper, we present a new harmonic mixing method based on psychoacoustic principles. Unlike existing commercial DJ-mixing software which determine compatible matches between songs via key estimation and harmonic relationships in the circle of fifths, our approach is built around the measurement of musical consonance at the signal level. Given two tracks, we first extract a set of partials using a sinusoidal model and average this information over sixteenth note temporal frames. Then within each frame, we measure the consonance between all combinations of dyads according to psychoacoustic models of roughness and pitch commonality. By scaling the partials of one track over +/- 6 semitones (in 1/8th semitone steps), we can determine the optimal pitch-shift which maximises the consonance of the resulting mix. Results of a listening test show that the most consonant alignments generated by our method were preferred to those suggested by an existing commercial DJ-mixing system.

Sound Examples

To give an impression about the mixes used in the listening test, here are five conditions of Mix 5 and the original, unmixed versions of the two tracks.

Unmixed Inputs

Input: Track 1

Input: Track 2

Mixed Conditions

Condition A: No Shift


Condition B: Key Match (Traktor)


Condition C: Dissonant


Condition D: Consonant (Sensory)


Condition E: Consonant (Sensory + Harmony)



For more information related to this topic, see here.

Funding Acknowledgments


RG was supported by a DEGA Young Scientists Grant (travel grant) an Erasmus SMP scholarship to conduct the research during a visit to INESC TEC.
MEPD was supported by FCT post-doctoral grant SFRH/BPD/88722/2012.
BS was supported by BMBF 01 GQ 1004B (Bernstein Center for Computational Neuroscience Munich).