deepGTTM-I: Local Boundaries Analyzer based on A Deep Learning Technique
نویسندگان
چکیده
Abstract. This paper describes a method that enables us to detect the local boundaries of a generative theory of tonal music (GTTM). Although systems that enable us to automatically acquire local boundaries have been proposed such as a full automatic time-span tree analyzer (FATTA) or σGTTM, musicologists have to correct the boundaries because of numerous errors. In light of this, we propose a novel method called deepGTTM-I for detecting the local boundaries of GTTM by using a deep learning technique. The experimental results demonstrated that deepGTTM-I outperformed the previous analyzers for GTTM in an F-measure of detecting local boundaries.
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