|Titel:||Real-time Machine Listening and Segmental Re-synthesis for Networked Music Performance||Sonstige Titel:||Echtzeit- maschinelles Zuhören und segmentierte Resynthese von Netzwerk-Musikvorführung||Sprache:||Englisch||Autor*in:||Alexandraki, Chrisoula||Schlagwörter:||networked music performance; machine listening; audio to score alignment; onset detection; segmental audio synthesis; Hidden Markov Models||GND-Schlagwörter:||Systematische Musikwissenschaft||Erscheinungsdatum:||2014||Tag der mündlichen Prüfung:||2014-11-17||Zusammenfassung:||
The general scope of this work is to investigate potential benefits of Networked Music Performance (NMP) systems by employing techniques commonly found in Machine Musicianship. Machine Musicianship is a research area aiming at developing software systems exhibiting some musical skill such as listening, composing or performing music. A distinct track of this research line, mostly relevant to this work, is computer accompaniment systems. Such systems are expected to accompany human musicians by causally analysing the music being performed and timely responding by synthesizing an accompaniment, or the part of one or more of the remaining members of a performance ensemble. The objective of the present work is to investigate the possibility of representing each performer of a dispersed NMP ensemble, by a local computer-based musician, which constantly listens to the local performance, receives network notifications from remote locations and re-synthesizes the performance of remote peers. Whenever a new musical construct is recognized at the location of each performer, a code representing that construct is communicated to all of the remaining musicians, as low-bandwidth information. Upon reception, the remote audio signal is re-synthesized by splicing pre-recorded audio segments corresponding to the musical construct identified by the received code. Computer accompaniment systems may use any conventional audio synthesis technique to generate the accompaniment. In this work, investigations focus on concatenative music synthesis, in an attempt to preserve all expressive nuances introduced by the interpretation of individual performers. Hence, the research carried out and presented in this dissertation lies on the intersection of three domains, which are NMP, Machine Musicianship and Concatenative Music Synthesis.
The dissertation initially presents an analysis of the current trends in all three research domains, and then elaborates on the methodology that was followed to realize the intended scenario. Research efforts have led to the development of BoogieNet, a preliminary software prototype implementing the proposed communication scheme for networked musical interactions. Real-time music analysis is achieved by means of audio-to-score alignment techniques and re-synthesis at the receiving end takes place by concatenating pre-recorded and automatically segmented audio units, generated by means of onset detection algorithms. The methodology of the entire process is presented and contrasted with competing analysis/synthesis techniques. Finally, the dissertation presents important implementation details and an experimental evaluation to demonstrate the feasibility of the proposed approach.
|URL:||https://ediss.sub.uni-hamburg.de/handle/ediss/5694||URN:||urn:nbn:de:gbv:18-71008||Dokumenttyp:||Dissertation||Betreuer*in:||Bader, Rolf (Prof. Dr.)|
|Enthalten in den Sammlungen:||Elektronische Dissertationen und Habilitationen|
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