M.I.N.U.E.T.: Procedural Musical Accompaniment for Textual Narratives

Maniktala, Mehak; Miller, Chris; Margolese-Malin, Aaron; Jhala, Arnav; Martens, Chris

doi:10.1145/3402942.3409602

Cited by 1 publication

(2 citation statements)

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“…One group of hybrid AI-AMG systems combine HMM methods with rule-based methods [28,44,53]. In these AI-AMG systems, the HMM method is used for the selection of musical chords, note length, and the octave range (the probability of playing a note from same/different octave) while musical features such as tempo, mode, and pitch range are tailored using musical rules for generating mood-specific music.…”

Section: Hybrid Systemsmentioning

confidence: 99%

“…In these AI-AMG systems, the HMM method is used for the selection of musical chords, note length, and the octave range (the probability of playing a note from same/different octave) while musical features such as tempo, mode, and pitch range are tailored using musical rules for generating mood-specific music. For example, the hybrid AI-AMG system proposed in [44] has deployed a combination of Markov model and a rule-based approach to compose affective music for a textual narrative segment. The textual narratives are first processed using sentiment analysis methods (deep learning models, support vector machine, naive Bayes classifier) to extract emotion/mood information, and subsequently, this sentiment information is used by the hybrid AI-AMG to compose sentiment-specific affective music.…”

Section: Hybrid Systemsmentioning

confidence: 99%

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AI-Based Affective Music Generation Systems: A Review of Methods, and Challenges

Dash¹,

Agres²

2023

Preprint

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Music is a powerful medium for altering the emotional state of the listener. In recent years, with significant advancement in computing capabilities, artificial intelligence-based (AI-based) approaches have become popular for creating affective music generation (AMG) systems that are empowered with the ability to generate affective music. Entertainment, healthcare, and sensor-integrated interactive system design are a few of the areas in which AI-based affective music generation (AI-AMG) systems may have a significant impact.Given the surge of interest in this topic, this article aims to provide a comprehensive review of AI-AMG systems. The main building blocks of an AI-AMG system are discussed, and existing systems are formally categorized based on the core algorithm used for music generation. In addition, this article discusses the main musical features employed to compose affective music, along with the respective AI-based approaches used for tailoring them. Lastly, the main challenges and open questions in this field, as well as their potential solutions, are presented to guide future research. We hope that this review will be useful for readers seeking to understand the state-of-the-art in AI-AMG systems, and gain an overview of the methods used for developing them, thereby helping them explore this field in the future.

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Section: Hybrid Systemsmentioning

confidence: 99%

Section: Hybrid Systemsmentioning

confidence: 99%