Functional Harmony Recognition of Symbolic Music Data with Multi-task Recurrent Neural Networks
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Previous works on chord recognition mainly focus on chord symbols but overlook other essential features that matter in musical harmony. To tackle the functional harmony recognition problem, we compile a new professionally annotated dataset of symbolic music encompassing not only chord symbols, but also various interrelated chord functions such as key modulation, chord inversion, secondary chords, and chord quality. We further present a novel holistic system in functional harmony recognition; a multi-task learning (MTL) architecture is implemented with the recurrent neural network (RNN) to jointly model chord functions in an end-to-end scenario. Experimental results highlight the capability of the proposed recognition system, and a promising improvement of the system by employing multi-task learning instead of single-task learning. This is one attempt to challenge the end-to-end chord recognition task from the perspective of functional harmony so as to uncover the grand structure ruling the flow of musical sound. The dataset and the source code of the proposed system is announced at https://github.com/Tsung-Ping/functional-harmony. 以前のchord recognitionについての研究は主にchord symbolにフォーカスを当てていたが、ハーモニーを更生するのに重要な特徴を見落としている。このfunctional harmony recognition problemに取り組むために、我々はchord symbolだけでなく、key modulation, chord inversion, secondary chords
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