All posts tagged “MIR

ABC_DJ @Sound and Music Computing, Cyprus






We’re glad to announce that our publication “Heuristic Algorithm for DJ Cue Point Estimation” will be part at the Sound and Music Computing Conference in Cyprus. The main theme of the conference focuses on the ability of sound and music to cross boundaries, to eliminate borderlines and overcome physical and digital limitations.

Our Dj Cue Point Module is an automatic track annotation and DJ mixing algorithm which was developed in the context of audio branding for in-store music delivery.

This publication was jointly realised by IRCAM and HearDis!.


ABC_DJ at Music Information Retrieval Berlin Meetup






On June 25th 2018 we present our knowledge-based music branding recommender system at MIR Berlin Meetup.  The ABC_DJ recommender system requirements significantly differ from traditional music recommenders: In our case, the perceived semantic expression of music titles is of main interest since it has to meet marketing intentions, whereas consumers’ personal preferences or emotional responses are of rather minor importance.

In order to address the ‘semantic gap’ between audio signal analysis and complex brand identities to be communicated by music to heterogeneous target groups, our system combines machine learning of music branding expert knowledge with audio signal analysis toolboxes’ output and population-representative ground truth data gathered by multinational online listening experiments.

The MIR Berlin Meetup will take place at The Hybrid Lab a work, event and experimentation space serving as cross-disciplinary exchange of art, science and technology.

ACM RecSys 2017

The ACM Recommender Systems conference (RecSys) is the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. Recommendation is a particular form of information filtering, that exploits past behaviors and user similarities to generate a list of information items that is personally tailored to an end-user’s preferences.