Fusing Web and Audio Predictors to Localize the Origin of Music Pieces for Geospatial Retrieval

Abstract

Localizing the origin of a music piece around the world enables some interesting possibilities for geospatial music retrieval, for instance, location-aware music retrieval or recommendation for travelers or exploring non-Western music – a task neglected for a long time in music information retrieval (MIR). While previous approaches for the task of determining the origin of music either focused solely on exploiting the audio content or web resources, we propose a method that fuses features from both sources in a way that outperforms stand-alone approaches. To this end, we propose the use of block-level features inferred from the audio signal to model music content. We show that these features outperform timbral and chromatic features previously used for the task. On the other hand, we investigate a variety of strategies to construct web-based predictors from web pages related to music pieces. We assess different parameters for this kind of predictors (e.g., number of web pages considered) and define a confidence threshold for prediction. Fusing the proposed audio- and web-based methods by a weighted Borda rank aggregation technique, we show on a previously used dataset of music from 33 countries around the world that the median placing error can be reduced from $1,\!815$ to 0 kilometers using K-nearest neighbor regression.