Papers › TräumerAI: Dreaming Music with StyleGAN

TräumerAI: Dreaming Music with StyleGAN

9 Feb 2021arXiv:2102.04680archive 2025-07-28

Dasaem Jeong, Seungheon Doh, Taegyun Kwon

The goal of this paper to generate a visually appealing video that responds to music with a neural network so that each frame of the video reflects the musical characteristics of the corresponding audio clip. To achieve the goal, we propose a neural music visualizer directly mapping deep music embeddings to style embeddings of StyleGAN, named Tr\"aumerAI, which consists of a music auto-tagging model using short-chunk CNN and StyleGAN2 pre-trained on WikiArt dataset. Rather than establishing an objective metric between musical and visual semantics, we manually labeled the pairs in a subjective manner. An annotator listened to 100 music clips of 10 seconds long and selected an image that suits the music among the 200 StyleGAN-generated examples. Based on the collected data, we trained a simple transfer function that converts an audio embedding to a style embedding. The generated examples show that the mapping between audio and video makes a certain level of intra-segment similarity and inter-segment dissimilarity.

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Code

jdasam/traeumerAI officialmentioned in paperpytorch report

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Tasks

Music Auto-Tagging

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Music Auto-Tagging TimeTravel Fellini 0..5sec 5 #1 of 1 Archive leaderboard report

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Methods

Adaptive Instance NormalizationConvolutionDense ConnectionsFeedforward NetworkPath Length RegularizationR1 RegularizationStyleGANStyleGAN2Weight Demodulation

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