AI in music reconstruction: Sentiment in pro versus amateur contexts

Abstract

This case study uses a comparative framework to examine the ethical implications of artificial intelligence (AI) in music reconstruction, expanding on prior research comparing audience sentiments across professional and amateur musical contexts. It investigates two distinct applications of AI in the domain of music. Study A utilises qualitative sentiment analysis of YouTube comments to assess spontaneous public responses to an AI-enhanced version of Queen's The Night Comes Down (2024 Mix – Single Version) , analysing the top 220 most-engaged comments collected in March 2025. Study B, based on previously collected data, employs a focus group methodology to examine structured discussions among experts regarding the AI-assisted reconstruction of semi-professional Austrian bands ( Schattenparker aus Wien and The Banana Trees ). A comparative analysis reveals a sharp divergence: while Study A showed predominantly negative reactions – commenters criticised pitch correction and AI-generated visuals as threats to authenticity, artistic integrity and Freddie Mercury's legacy – Study B participants largely welcomed AI as a democratising tool for enhancing and preserving amateur recordings. This contrast highlights the complex ethical and cultural tensions surrounding AI's role in creative industries, particularly the balance between innovation, artistic authenticity and audience trust. While AI-assisted reconstructions hold potential for democratising music production and archival preservation, these divergent responses underscore the need for clearer ethical guidelines. To address these concerns, we propose a voluntary labelling scheme as a metaphorical traffic-light to disclose the degree of AI intervention. We further recommend future empirical research to test such transparency mechanisms across genres, cultures and production settings.

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