We propose an interactive algorithm that musically accompanies musicians based on the matching of expressive feature patterns to existing archive recordings. For each accompany music segment, multiple realizations with different musical characteristics are performed by master music performers and recorded. Musical expressive features are extracted from each accompany segment and its semantic analysis is obtained using music expressive language model. When the performance of system user is recorded, we extract and analyze musical expressive feature in real time and playback the accompany track from the archive database that best matches the expressive feature pattern. By creating a sense of musical correspondence, our proposed system provides exciting interactive musical communication experience and finds versatile entertainment and pedagogical applications.

Authors: Bocko, Gregory; Bocko, Mark F.; Headlam, Dave;Lundberg, Justin; Ren, Gang
Affiliations: Dept. of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA; Dept. of Music Theory, University of Rochester, Rochester, NY, USA(See document for exact affiliation information.)
AES Convention: 129 (November 2010) Paper Number: 8256
Publication Date: November 4, 2010 Import into BibTeX
Subject: Signal Analysis and Synthesis
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=15678

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