AI MUSIC FOR HUMANOID SONGS AND IT'S QUALITY

Title: AI Music for Humanoid Songs Objective:The study aims to investigate the potential of artificial intelligence in generating, composing, and performing music tailored for humanoids—robots, virtual avatars, and AI-assisted performers. The objective is to design adaptive music systems that integrate machine learning, natural language processing, and sound synthesis to create songs that are not only technically precise but also emotionally resonant. Subjective:This research explores the cultural, emotional, and perceptual dimensions of AI-generated humanoid music. It examines how audiences interpret the authenticity, creativity, and emotional depth of songs performed by humanoids, and how such experiences reshape the relationship between humans, technology, and art. Scope:The scope includes: AI-based music composition and lyric generation. Real-time performance by humanoid robots and digital avatars. Cross-cultural and multilingual song adaptation. Applications in entertainment, therapy, education, and human-robot interaction. Comparative analysis of AI-generated versus human-generated music in terms of creativity, acceptance, and innovation. Expected Outcome:The study is expected to demonstrate that AI can serve as a powerful creative partner, enabling humanoids to express music in ways that enhance human engagement, broaden cultural accessibility, and redefine artistic collaboration. Outcomes will include new frameworks for AI-driven music production, insights into emotional perception of AI songs, and practical models for integrating humanoid music into future society.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-29
DOI
https://doi.org/10.5281/zenodo.17227444
Primary Topic
Economic and Technological Innovation
Type
article
Field-Weighted Citation Impact
0.00
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article

AI MUSIC FOR HUMANOID SONGS AND IT'S QUALITY

Zenodo (CERN European Organization for Nuclear Research)
Economic and Technological Innovation
article

AI MUSIC FOR HUMANOID SONGS AND IT'S QUALITY

article en

Abstract

Title: AI Music for Humanoid Songs Objective:The study aims to investigate the potential of artificial intelligence in generating, composing, and performing music tailored for humanoids—robots, virtual avatars, and AI-assisted performers. The objective is to design adaptive music systems that integrate machine learning, natural language processing, and sound synthesis to create songs that are not only technically precise but also emotionally resonant. Subjective:This research explores the cultural, emotional, and perceptual dimensions of AI-generated humanoid music. It examines how audiences interpret the authenticity, creativity, and emotional depth of songs performed by humanoids, and how such experiences reshape the relationship between humans, technology, and art. Scope:The scope includes: AI-based music composition and lyric generation. Real-time performance by humanoid robots and digital avatars. Cross-cultural and multilingual song adaptation. Applications in entertainment, therapy, education, and human-robot interaction. Comparative analysis of AI-generated versus human-generated music in terms of creativity, acceptance, and innovation. Expected Outcome:The study is expected to demonstrate that AI can serve as a powerful creative partner, enabling humanoids to express music in ways that enhance human engagement, broaden cultural accessibility, and redefine artistic collaboration. Outcomes will include new frameworks for AI-driven music production, insights into emotional perception of AI songs, and practical models for integrating humanoid music into future society.

Zenodo (CERN European Organization for Nuclear Research)
University of Messina (IT), Small and Medium Enterprises Development Organization (TR)
Quality Education
Openalex Percentile: Top 34%
Economic and Technological Innovation
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AI MUSIC FOR HUMANOID SONGS AND IT'S QUALITY · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS