All posts tagged “MIR

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.