Generating Music with Emotion Using Transformer

Pangestu, Mentari; Suyanto, Suyanto

doi:10.1109/ic2se52832.2021.9791928

Cited by 8 publications

(2 citation statements)

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“…The solution used different conditioning from the Transformer and used a symbolic music dataset annotated by continuous arousal and valence values. The use of a Transformer to generate music with a controlled emotion was proposed by Pangestu et al in [30]. Emotions were described using three categories-negative, neutral, and positive.…”

Section: B Emotion-based Music Generationmentioning

confidence: 99%

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Generating Polyphonic Symbolic Emotional Music in the Style of Bach Using Convolutional Conditional Variational Autoencoder

Grekow

2023

IEEE Access

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In times of increasing human-machine interaction, the implementation of emotional intelligence in machines should not only recognize and track emotions during human interaction, but also respond with appropriate emotional content. Machines should be able to react and respond to human emotions. Music generation with a specific emotion is part of this task. This article presents the process of building a system generating polyphonic music content of a specified emotion using a conditional variational autoencoder and convolutional layers. The process of preparing a database of training examples with compositions by Johann Sebastian Bach, selecting and conducting transformations of musical examples was described. Annotation with emotion labels was done by music experts with a university music education. The four emotion labelshappy, angry, sad, relaxed -corresponding to the four quadrants of Russell's model were used. The process of coding symbolic music examples into a time-pitch matrix representation, but also the structure of the built variational autoencoder, was described. Experiments on the implementation of different convolutional layers intended for visual analysis of the representation of music examples were presented. The generated emotional music files were evaluated using metrics and expert opinions.

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Section: B Emotion-based Music Generationmentioning

confidence: 99%

“…Also limiting the length of the musical examples in the training dataset was used in [39]. A data usage limitation that only uses music with a 4/4 time signature was used in [27], [30], [39]. The transposition of all examples into a single common key as preprocessing is not used often.…”

Section: A Symbolic Music Datasetmentioning

confidence: 99%