Discovering Research Trends in Artificial Music Intelligence: Perspectives of Digital Humanities through Computational Text Mining
Creators: name: Das, Anup Kumar
name: Das, Suman
name: Tripathi, Manorama
Publisher: name: Zenodo
Year: 2023

description: Extended Abstract:

Motivation: Music plays a central role in managing an easy-to-use stress release tool in modern times. Many studies have shown that listening to appropriate music can release stress(1,2). In recent decades automatic music composition has increased significantly with the advancement of OpenAI last year, AI music generators have been used for a variety of purposes such as background music, soundtracks, or sound effects. Content creators, musicians, advertisers, event organizers, fitness instructors, and more use them to create unique, customizable music. Possible drawbacks for creative industries include diminished emotional depth in music, reduced originality, creative homogenization, weakened human connection, and overreliance on algorithms. AI has significantly influenced the music we enjoy, including the artists we follow, through AI algorithms that drive personalized recommendation systems on streaming platforms. Gen Z individuals predominantly use social media for music discovery, instead of traditional platforms like radio and TV. Algorithms also play a role in diversifying the user’s musical taste palette.

Problem Statement: The research on music and computer application, deep learning or machine learning related studies are paramount in understanding humanistic problems faced by music producer, composer, listener, which is useful for planning and producing automated machine produced music. AI has transformed the way the music industry was functioning a decade ago. This study aspires to present an exhaustive scholarly investigation into AI, ML/DL and music over the past three decades.



To identify the knowledge structure of Artificial Music Intelligence literature by developing schematic diagrams of the relationship between keywords from a macro perspective.

To investigate the main topic of artificial intelligence and music correlation during 1991-2022 through web of science bibliographic data.

To explore the future direction of music and computational application for music producer to listener through digital humanities perspectives.


Methods/Study Design: The study seeks to establish the twin goals of artificial music intelligence subject discovery through quantitative content study using text network analysis, especially topic modelling and bibliometric method will be employed to examine the publication trend and the most influential articles in this field. Using the topic modelling approach, we will analyze a corpus of research articles on AI and automatic music performance (Web of Science database), identify the main topics within the field and evaluate their evolution over time, co-occurrence, impact (based on citations), and prestige (based on journal ranking and Impact Factor). LDAvis and Structural topic modelling (STM) techniques are instrumental in comprehending the developed and emerging topics. 

Predictions and Results: The keywords with the most frequency and the highest networking degree in centrality will provide the knowledge structure of research on artificial music intelligence. The LDA and STM model will give a descent understanding of hierarchical structure of AL and music research.  Text mining method will offer us to discover the computational tool application in music through the lens of Digital Humanities and provide a sociological, economic and cultural perspective of the growing field.

Conclusions: The results will aid music researchers and educators in comprehending trends in music intelligence education. This will help educate future composers to create impressive, human cognition music intelligence. The automated method cannot replace human importance, therefore human-centered computing is crucial. Our study will suggest that the relationship between machine and human is complementary.