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Papers

Music Sentiment Transfer

2021-10-12 · Miles Sigel, Michael Zhou, Jiebo Luo

Music sentiment transfer is a completely novel task. Sentiment transfer is a natural evolution of the heavily-studied style transfer task, as sentiment transfer is rooted in applying the sentiment of a source to be the new sentiment for a target piece of media; yet compared to style transfer, sentiment transfer has been only scantily studied on images. Music sentiment transfer attempts to apply the high level objective of sentiment transfer to the domain of music. We propose CycleGAN to bridge disparate domains. In order to use the network, we choose to use symbolic, MIDI, data as the music format. Through the use of a cycle consistency loss, we are able to create one-to-one mappings that preserve the content and realism of the source data. Results and literature suggest that the task of music sentiment transfer is more difficult than image sentiment transfer because of the temporal characteristics of music and lack of existing datasets.

📄 PDF Abstract BibTeX arXiv:2110.05765

Code (1)

milesigel/audio-sentiment-transfer 공식 구현 pytorch

Tasks

Style Transfer

Methods 이 논문이 사용한 방법론

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Batch Normalization 설명 없음
Residual Connection 설명 없음
PatchGAN 설명 없음
Residual Block Residual Blocks are skip-connection blocks that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. They were introduced…
Tanh Activation 설명 없음
Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…
Sigmoid Activation 설명 없음

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