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Emotion-Driven Melody Harmonization via Melodic Variation and Functional Representation

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arxiv 2407.20176 v2 pith:CMPBM2RA submitted 2024-07-29 cs.SD cs.AIeess.AS

classification cs.SDcs.AIeess.AS
keywords melodyrepresentationkeysmusicconveyemotion-drivenemotionalfunctional
verification ladder T0 review T1 audit T2 compute T3 formal

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Emotion-driven melody harmonization aims to generate diverse harmonies for a single melody to convey desired emotions. Previous research found it hard to alter the perceived emotional valence of lead sheets only by harmonizing the same melody with different chords, which may be attributed to the constraints imposed by the melody itself and the limitation of existing music representation. In this paper, we propose a novel functional representation for symbolic music. This new method takes musical keys into account, recognizing their significant role in shaping music's emotional character through major-minor tonality. It also allows for melodic variation with respect to keys and addresses the problem of data scarcity for better emotion modeling. A Transformer is employed to harmonize key-adaptable melodies, allowing for keys determined in rule-based or model-based manner. Experimental results confirm the effectiveness of our new representation in generating key-aware harmonies, with objective and subjective evaluations affirming the potential of our approach to convey specific valence for versatile melody.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Audio Deepfake Detection to AI-Generated Music Detection -- A Pathway and Overview

    cs.SD 2024-11 conditional novelty 5.0 of 10

    A review of AI-generated music detection that proposes intrinsic music features and multimodal fusion as the basis for adapting audio deepfake detection methods.

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