ONE IN THE JUNGLE: DOWNBEAT DETECTION IN HARDCORE, JUNGLE, AND DRUM AND BASS

ONEINTHEJUNGLE:DOWNBEATDETECTIONINHARDCORE, JUNGLE,ANDDRUMANDBASS 1;2 3 1;2JasonA.Hockman ,MatthewE.P.Davies ,andIchiroFujinaga 1Centre for Interdisciplinary Research in Music Media and Technology (CIRMMT) 2Distributed Digital Archives and Libraries (DDMAL), McGill University, Montreal, Canada 3Sound and Music Computing Group, INESC TEC, Porto, Portugal jason.hockman@mail.mcgill.ca, mdavies@inescporto.pt, ich@music.mcgill.ca ABSTRACT In this study, we present a downbeat detection model created with the intention of finding downbeats within Hardcore, jungle, and drum and bass (HJDB) are fast- music containing breakbeats, and provide a comparison of paced electronic dance music genres that often employ its performance against four pre-existing algorithms on a resequenced breakbeats or drum samples from jazz and database of 206 HJDB excerpts. We view this as a first step funk percussionist solos. We present a style-specific in an automated analysis of the musical surface of HJDB method for downbeat detection specifically designed for from a computational musicology perspective, towards the HJDB. The presented method combines three forms of eventual goal of understanding how individual artists use metrical information in the prediction of downbeats: low- breakbeats (e.g., slice ordering and pitch adjustment) in level onset event information; periodicity information from modern music. beat tracking; and high-level information from a regression model trained with classic breakbeats.