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From Analogue to Algorithm: The Metamorphosis of Music

Production Techniques—An Integrated Literature Review

Article in Journal of Creative Communications · October 2024

DOI: 10.1177/09732586241281205

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Original Article

From Analogue to Algorithm: Journal of Creative Communications

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The Metamorphosis of Music © 2024 MICA-The School of Ideas
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Linto Thomas1 and V. Vijay Kumar1

Abstract
This research explores the transformation of music production (MP) from traditional to smart digital
approaches. The background of the study is rooted in the rapid digital advancements in MP over the
last few decades, leading to a more robust and compact MP progression. The research focuses on
understanding the music industry’s process and the impact of technology on MP, particularly artificial
intelligence (AI). The research methodology employed is an Integrative Literature Review, based on both
Scopus and Web of Science databases. After thoroughly examining titles and abstracts, 745 documents
were identified as bibliographic data. From these, 40 selected articles were synthesised and analysed
to identify patterns, influential authors, prominent journals and countries, dominant intellectual themes
in MP, literature clustering and research gaps. The study classifies MP into four clusters: humanistic,
technological, social and cultural and industrial perspectives. Each lens offers a unique vantage point to
understand the intricate tapestry of the musical landscape. The findings reveal that AI has revolutionised
MP, contributing to the creation of novel sounds, genres and music arrangements. It has also facilitated
cross-cultural collaborations and reshaped the music business model. These findings imply that the
intersection of technology and the music industry has transformed music creation, consumption and
monetisation, presenting both challenges and opportunities for stakeholders. The study concludes that
the future of MP is a captivating fusion of human creativity and technological prowess, where tradition
and innovation coalesce to propel musical revolution to new heights.

Keywords
Music, music production, music creation, AI music, AI music generation, music automation, intelligent
music composition

Introduction
Music production (MP) has undergone significant digital advancements in recent decades, becoming more
robust in terms of resilience, flexibility and high-quality output (Holmes & Holmes, 2008) and more

1
School of Communications, XIM University, Bhubaneswar, Odisha, India

Corresponding author:
V. Vijay Kumar, School of Communications, XIM University, Bhubaneswar, Odisha 752050, India.
E-mail: vijaykumarvijayan@gmail.com
2 Journal of Creative Communications

compact through miniaturisation and integration of equipment, which enhance portability and space effi-
ciency (Hosken, 2014). MP encompasses the creative process of composing, recording and manipulating
audio elements (Burgess, 2014; Leung, 2005), with each song going through this intricate process (Leider,
2004). The digital era has led to an exponential increase in MP, with platforms like Spotify receiving
approximately 60,000 new tracks daily (Ingham, 2021). The MP journey involves multiple stages, from
conceptualisation to mixing and mastering, demanding creativity and a systematic approach. This process
has evolved from magnetic recording through acoustic and analogue methods to the current digital era
(Wiggins, 1995) and is entering an artificial intelligence (AI)–driven phase (Rane, 2023). Understanding
each stage of this complex and time-intensive process is crucial for aspiring music producers.
Landmark events in AI-created music include the completion of Beethoven’s Tenth Symphony (Kelly,
2021) and the Tokyo 2020 Beat, showcasing the integration of AI in creative composition. These devel-
opments reflect the ongoing transformation of MP techniques and the increasing role of technology in
shaping the music industry’s future. The evolution of MP highlights its multifaceted nature, encompass-
ing technological advancements, creative processes and the changing landscape of music distribution
and consumption. Many musicians are exploring innovative methods to incorporate AI technology into
music (Pachet, 2003). Indeed, several software applications can replicate the artistic styles of different
composers, while others employ AI and machine-learning algorithms to create music pieces and songs
(Briot & Pachet, 2018). AI has significantly transformed the MP landscape, with various AI music gen-
erators such as Amper Music, AIVA, Soundful and others gaining prominence globally (McFarland,
2024). These tools leverage vast datasets to identify patterns and trends beyond human perception, gen-
erating music based on specific prompts. The open-source nature of many of these technologies fosters
continuous improvement and accessibility. AI’s capabilities extend to predicting a song’s success through
audience sentiment analysis. Notably, AI empowers amateur musicians by enhancing their creative pro-
cesses and musical endeavours (McFarland, 2024). This technological integration is engaging diverse
stakeholders, including musicians, sound designers, recording studios and researchers, all of them are
pursuing innovative methods to integrate AI into the music and sound landscape. The dynamic interplay
is reshaping the future of MP and consumption. As researchers, we are particularly interested in knowing
both the integration of AI into music and the scrutiny of this process to comprehend its broader implica-
tions. The principal research questions are, how does the integration of AI in MP influence the creative
process and industry practices? And how has the metamorphosis from analogue to algorithmic tech-
niques transformed the evolution and practices of MP? These questions provide the foundation for the
sub-research questions, which are designed to deepen the understanding of the inquiry: Identifying the
pivotal influential authors in the field of MP, determining, which journals and countries contribute
the most significant works in MP, analysing the prevailing intellectual themes within the domain, inves-
tigating the clustering patterns in MP literature and pinpointing research gaps that present novel direc-
tions for future scholarly exploration in MP.

Music and MP
Music is a complex, evolving concept encompassing melodic, harmonic and social aspects that vary
across cultures (Bakan, 2007). It consists of essential elements such as sound, silence, artistry and organ-
isation, creating both physical and intellectual attributes (Tilton, 2008). Physical attributes involve
the production and manipulation of sound through instruments, equipment and technology, while
intellectual attributes encompass cognitive processes including creativity and interpretation. Music
serves various purposes, including entertainment and religious rituals, requiring active engagement
Thomas and Kumar 3

(Davies, 1997). As an expressive language, it uses pitch and beat to create aesthetic experiences. The
term ‘production’, rooted in Latin, means to bring or lead something forward (Lewis & Short, 1879), and
in the context of music, it refers to the process of developing and refining recorded music from songwrit-
ing to mixing (Ludwig & Geisthardt, 2022). Technological advancements have made MP more accessi-
ble (Katz, 2010), with the progression from analogue to digital technology revolutionising the industry.
The introduction of digital audio workstations (DAW) and musical instrument digital interface (MIDI)
has transformed MP practices (Tondelli, 2016). This transformation optimised recording, editing and
compositional processes, thereby significantly enhancing both creative potential and operational effi-
ciency within the field. AI has emerged as a powerful tool in MP, enabling composition assistance, auto-
mation, and even generating melodies and harmonies (Cope, 2004). This ongoing interplay between
technological and artistic innovation continues to drive the evolution of MP. The integration of AI and
other digital technologies has not only enhanced creative possibilities but also reshaped the landscape
of music creation, distribution and consumption.
The evolution of MP is shaped by foundational philosophical ideas. Aestheticism values beauty and
sensory experience, inspiring music that is emotionally resonant and artistically pleasing (Levinson,
2018). Utilitarianism focuses on maximising happiness by guiding the creation and distribution of music
that brings joy to a broad audience and encouraging technological innovation for accessibility
(Hesmondhalgh, 2013; Mill, 1863). Postmodernism, with its emphasis on experimentation and individ-
ual expression, drives genre evolution and promotes independent and Do It Yourself production
(Middleton, 2002). These philosophies guide the MP industry by fostering artistic exploration, audience-
centric approaches and embracing diversity and innovation (Frith, 1996).
This study examines the adoption and impact of music technology through the integrated application of
two seminal theoretical frameworks: the diffusion of innovation theory, which explores the spread and
acceptance of new technologies (Rogers, 2003; Smith & Green, 2021). We examined the patterns of adop-
tion of AI and digital tools in MP, emphasising the role of social networks and industry gatekeepers (Lee,
2018). The theory elucidated how new technologies like AI music generators (e.g., Amper Music and
AIVA) are adopted within the music industry, focusing on the influence of early adopters and industry
influencers in promoting these innovations. The uses and gratification theory investigates user motivation
and satisfaction (Katz et al., 1974) behind new technologies in MP, and the satisfaction derived from their
use. It illuminated the interplay between creativity and audience engagement (Burgess, 2013; Watson,
2015). We explored how musicians and producers use AI tools to enhance their creative processes, and how
these tools fulfil various needs such as ease of use, efficiency and audience engagement. The theory also
helped explain the benefits of engaging with new music technologies, aligning with tangible and intangible
gratifications. This dual theoretical approach provides a comprehensive framework for understanding the
evolution of MP, offering valuable insights for both practitioners and researchers in the field.

Research Questions
This study seeks to examine the transition from analogue to algorithmic techniques, and how the shift
has shaped the evolution and practices of MP. Using a combination of keywords related to MP, AI music
and intelligent music composition, the authors have curated a dataset of relevant journal articles. This
approach exposes the identification of recent trends and emerging themes in MP research, leading to
research questions and sub-questions. These questions are designed to comprehensively analyse the
transformation in MP techniques and their impact on the field. Identifying key authors, leading journals
and countries, highlights influential contributors and research hubs. Understanding dominant themes and
4 Journal of Creative Communications

literature clusters reveals prevailing trends and methodologies. Further, identifying research gaps ensures
the study addresses current and future needs in AI-driven MP.

RQ1: How has the metamorphosis from analogue to algorithmic techniques transformed the
evolution and practices of MP?
RQ1a: Who are the key influential authors in the field of MP?
RQ1b: Which journals and countries produce the prominent works in MP?
RQ1c: Which intellectual themes are dominating in MP?
RQ1d: How is the literature on MP clustered?
RQ1e: What are the research gaps offering novel directions for future studies in MP?
RQ2: How is AI reshaping the creative process, ownership and industry practices in MP?

Methodology: Integrated Literature Review

The Integrative Literature Review (ILR) is a comprehensive research methodology (Lubbe et al., 2020)
that synthesises and analyses existing literature on a specific topic, going beyond mere summarisation to
identify patterns, connections and gaps in the current body of knowledge (McCaffrey et al., 2020). This
approach is particularly valuable for addressing mature and niche fields of study (Baltazar & Saarikallio,
2016), enabling researchers to conceptualise themes, classify them and synthesise the literature effec-
tively. In the MP research, ILR proves essential, due to the field’s multidisciplinary nature, spanning
various disciplines each with unique perspectives on music and its technological implications (Waskito
& Karja, 2023). The scattered nature of MP literature across diverse domains renders a manual review
impractical, necessitating the use of bibliometric techniques to identify existing literature and future
research directions. This study employs ILR for two primary reasons: first, to accommodate the wide
range of relevant keywords and titles (e.g., music composition, AI music, intelligent music composition,
music creation and music automation); and second, to address the breadth of MP research across domains
including social science, computer science, psychology, economics and many others. Consequently, an
integrated approach combining bibliometric analysis with an in-depth literature review is adopted,
allowing for a critical examination, analysis and synthesis of the existing MP literature across its multi-
faceted landscape.

Method and Material

The process of the ILR encompasses several distinct stages, which are as follows. The first stage, data
retrieval, involves the meticulous identification of scholarly papers. This is achieved by applying
exclusion filters to ensure that only the most pertinent articles are selected in the required format. Once
the relevant articles are gathered, the next stage is data processing. The acquired data undergo further
processing, involving actions such as merging and cleansing, which are executed with the aid of
computational tools within RStudio. It is commonly used for data processing due to its strong data
handling capabilities, but it is not exclusive; other tools such as Python, MATLAB, SAS or Excel can
also be used based on the study’s needs and the user’s preference. By merging various data sources, a
comprehensive dataset is created, while cleansing removes irrelevant information. Following data
processing, the focus shifts to data analysis and visualisation. This stage transforms processed data into
meaningful insights, leading to detailed analysis and visualisation of the information, which helps obtain
quantitative and graphical details for conducting an in-depth study (Milani et al., 2020).
Thomas and Kumar 5

To enhance the reliability and trustworthiness, we decided to extract data from Scopus and Web of
Science (WoS). Mongeon and Paul-Hus (2015) and Echchakoui (2020) have argued to combine both
databases for a comprehensive bibliometric analysis. The key benefit of using both databases is that
they complement each other, giving a more comprehensive view of the available literature. The reason
behind the selection of both databases for this study is that they provide extensive, high-quality, peer-
reviewed sources, ensuring the research is based on credible and authentic information. The advanced
search capabilities and citation analysis tools enable precise exploration of interdisciplinary research,
which is crucial for tracking the evolution of MP techniques. Additionally, these databases are fre-
quently updated, giving access to the latest developments and trends in the field of study. To make sure
the search is as precise as possible, we carefully selected the keywords for the database search and
experimented with different combinations to optimise the search. Using the finalised keywords in both
Scopus and WoS during October 2023, we retrieved 2,258 journal articles in Scopus and 1,419 in
WoS. Then, we filtered the years (1965 to 2023), subject area, document type and language. Ultimately,
545 papers from Scopus and 410 from WoS were secured. The selection of the year 1965 as the start-
ing point for this study is rooted in its historical significance in the field of MP. This period marks the
introduction of multitrack recording techniques and early electronic music experiments, which have
profoundly influenced subsequent developments in MP technology. A pictorial representation is given
in Figure 1.
To maintain the data integrity and ease of management, the metadata of these papers were down-
loaded in Bibtex (music production. bib) and Plaintext formats (Music production. txt) from the
respective Scopus and WoS databases. The extracted data were normalised by setting similar tags for
both Scopus and WoS. The researchers did it manually, following the guidelines provided on the
Biblioshiny web application. Subsequently, the corrected documents were merged, and 148 duplicates
were removed using Bibliometrix packages in RStudio. Once the data integrity was confirmed,
manual database analysis was resorted to creating two more exclusion criteria to make the database
more precise to the topic. The first exclusion criterion was that any publication that lacked reference

Figure 1. Application of ILR on MP.

6 Journal of Creative Communications

to ‘Music’, ‘Music Production’, ‘Music Creation’, ‘Music Generation’, ‘Music Automation’, ‘AI
Music’ and ‘Intelligent Music Composition’ was excluded. Hence, a careful review of the titles and
keywords was done to determine the publication’s relevance. The second was to exclude any article
with ambiguous titles (e.g., The Phantom Influence: Uncovering Hidden Drivers of Behavioral
Outcomes of …. — it does not relate to MP). As a result, 62 articles were identified and deleted. After
thoroughly examining titles and abstracts, 745 documents as bibliographic data for bibliometric analy-
sis were selected and imported into the Bibliometrix package in RStudio. From these filtered 745
articles, 40 were selected for in-depth analysis based on the criteria of citation counts, relevance to the
research questions and contributions to the field. Our analysis involved a detailed synthesis and the-
matic examination, highlighting key trends, technological impacts and theoretical advancements.
Bibliometric analysis has gained prominence across various disciplines in recent years (Aswin et al.,
2022; Chhillar & Aguilera, 2022; Jimma, 2023; Maheshwari et al., 2023; Pradana et al., 2022; Salam
et al., 2023; Sharova et al., 2021; Victor et al., 2023; Zahra et al., 2021; Zhavoronok et al., 2022).
Originally defined by Pritchard (1969) as a ‘statistical method to study books and other forms of com-
munication’, the concept has since evolved. Ellegaard and Wallin (2015) broadened it to a ‘quantitative,
mathematical and statistical method for classifying the aspects of scientific knowledge’. Donthu et al.
(2021) emphasised its ability to make sense of large volumes of unstructured data, while Victor et al.
(2023) noted its capacity to enhance research robustness within specific disciplines. This study employs
bibliometric analysis, categorised under performance analysis and science mapping to identify emerging
themes and research streams in MP, addressing the need to comprehend literature trends, identify gaps
and propose future research directions in the evolving field of MP.

Scientific Production, Trends and Source Patterns: An Analysis

The following provides a comprehensive overview of the landscape in the scientific production of the MP
field presenting prominent authors, pinpointing influential works that have shaped the discourse and high-
lighting the countries with significant contributions. Trends in scientific production identify and explain
emerging intellectual themes and research streams in this field. This study is based on 745 journal articles,
but it is fascinating to note that MP is still a developing domain/area, and there are a lot of contributions
from diverse groups of authors. 1,101 authors who collectively shaped the body of literature in this domain
from 1977 to 2023 have been listed (Figures 2 and 3). A total of 745 scientific articles were collected from
253 different sources (journals) published between 1977 and 2023. The top 10 journals are provided in
Table 1. The impact of these journals is evaluated through a metric known as h-index. The AES: Journal of
the Audio Engineering Society is highly reputed and influential in the field of MP; this journal is dedicated
exclusively to the field of MP and audio engineering, including audio technology, sound recording, repro-
duction, processing and overall science of audio and MP. Although it focuses on audio engineering, it often
incorporates interdisciplinary research. It contains theoretical and practical suggestions for the audio
medium. It serves as a valuable resource for both academia and practice in the audio engineering and MP
industry. Among the top 10 journals, except for Frontiers in Psychology, all are closely related to MP,
music and its impact on technology, human culture, emotions and society.

Key Contributors: Influential Authors, Significant Works and Principal Countries

Out of 1,101 authors, 324 have single-authored publications. The rest 777 have multi-authored
publications. The authors’ productivity and contributions are based on the h-index value. Herbst of the
Thomas and Kumar 7

Figure 2. Annual Scientific Production from 1977 to 2023.

Figure 3. Overview of the Bibliometric Data Retrieved and Merged from Scopus and WoS.
Note: The first document (article) on MP is from 1977. Performance analysis measures the contributions of authors, institutions,
countries and journals. It is descriptive and measures the number of publications and citations per year. In measuring the
publications and the citations, one can analyse the productivity, influence and impact of a particular document, author or institution.
The metrics include but are not limited to total publications, number of contributing authors, total citations, collaboration index,
h-index, g-index and m-index (Donthu et al., 2021).

Department of Music and Design Arts at the University of Huddersfield holds five publications which
have been cited 42 times. Pachet from Spotify Creator Technology Research Lab follows him with four
publications cited 109 times. The 10 authors, including Herbst and Pachet, emerged as the most influential
contributors in the field of MP (Table 2). An analysis of both local and global citations was conducted to
identify the key publications that have made substantial contributions to the field of MP. The top 10
8 Journal of Creative Communications

Table 1. Top Ten Ranked Journals.

Journal h_index g_index m_index TC NP PY_start

AES: Journal of the Audio Engineering Society 7 11 0.184 129 13 1986
Frontiers in Psychology 6 12 0.6 145 14 2014
Journal of Music, Technology and Education 6 8 0.5 76 16 2012
Journal of New Music Research 6 12 0.261 216 12 2001
Popular Music and Society 6 12 0.128 153 16 1977
Organised Sound 5 10 0.208 102 14 2000
Computer Music Journal 4 9 0.105 345 9 1986
Ethonomusicology Forum 4 5 0.308 32 6 2011
Journal of Music Technology and Education 4 4 0.308 23 4 2011
Metal Music Studies 4 7 0.4 55 7 2014
Notes: h_index measures productivity and citation impact of publications. It provides objectivity to a journal’s scientific influence
in a particular domain (Victor et al., 2023).
Recently, the g_index and m_index have been created to measure the overall citation impact of the journals.
g_index improves on the h-index by giving more weightage to highly cited papers, ensuring that an author’s top papers are
adequately reflected in the score.
m_index is derived from the h-index, calculated as the h-index divided by the number of years a researcher has published.
TC, total citations (the total number of times a researcher’s publications have been cited by others, reflecting the overall impact
and influence of the work); NP, number of publications (the total count of scholarly articles, papers, or works that a researcher
has published); PY_start, start year (this refers to the year in which a researcher started publishing).

Table 2. List of Influential Authors.

Authors h_index g_index m_index TC NP

Herbst J 4 5 0.5 42 10
Pachet F 4 4 0.308 109 4
Bull A 3 4 40 4
Conklin D 3 3 0.375 31 3
Fazekas G 3 4 48 4
Mynett M 3 3 0.75 14 5
Reiss J 3 4 0.333 51 4
Sandler M 3 6 51 6
Van K A 3 3 0.333 18 3
Vālimãki V 3 3 0.25 144 3
Notes: h_index measures productivity and citation impact of publications. It provides objectivity to a journal’s scientific influence
in a particular domain (Victor et al., 2023).
Recently, the g_index and m_index have been created to measure the overall citation impact of the journals.
g_index improves on the h-index by giving more weightage to highly cited papers, ensuring that an author’s top papers are
adequately reflected in the score.
m_index is derived from the h-index, calculated as the h-index divided by the number of years a researcher has published.
TC, total citations (the total number of times a researcher’s publications have been cited by others, reflecting the overall impact
and influence of the work); NP, number of publications (the total count of scholarly articles, papers, or works that a researcher
has published).

significant works are presented in Table 3. All these works highlight the evolving relationship between
technology and music. Table 4 provides the top 10 countries that made significant contributions to the
literature on MP, with a total of 35 countries contributing to the MP literature. The United Kingdom leads
with 109 articles, followed by the United States with 83. The other prominent countries are China (30),
Thomas and Kumar 9

Table 3. List of Significant Works.

Local Global LC/GC

Document DOI Year Citations Citations Ratio (%)
Delgado M, 2009, Expert 10.1016/j.eswa.2008.05.028 2009 5 25 20
Systems with Applications
Saari P, 2016, IEEE Tractions 10.1109/taffc.2015.2462841 2016 2 23 8.7
on Affective Computing
Burgess RJ, 2017, Singer-Song https://bookscouter.com/ 2017 2 2 100
Writer Handbook book/9780199921720-the-art-of-
music-production-the-theory-and-
practice?type=sell
Yang Lc, 2020, Neural 10.1007/s00521-018-3849-7 2020 2 45 4.44
Computing and Applications
Power D, 2002, Environment 10.1068/a3529 2002 1 74 1.35
and Planning A: Economy
and Space
Hamanaka M, 2006, Journal of 10.1080/09298210701563238 2006 1 85 1.18
New Music Reserach
Brandellero AMC, 2011, Area 10.1111/j.1475-4762.2011.01057.x 2011 1 16 6.25
Pachet F, 2011, Constraints 10.1007/s10601-010-9101-4 2011 1 49 2.04
Mcintyre P, 2011, Journal of 10.1386/jmte.4.1.77_1 2011 1 9 11.11
Music, Technology & Education
V ālimãki V, 2012, IEEE 10.1109/tasl.2012.2189567 2012 1 133 0.75
Transactions on Audio Speech
and Language Processing
Notes: ‘Local citations’ assesses a particular document’s significance and prominence within the selected bibliographic data of
a review. It measures the number of citations a document receives from within the corpus selected for the analysis (Donthu
et al., 2021).
‘Global citations’ indicates the general impact of a particular document. It assesses the prominence by measuring the citations
received from different fields in the databases.

Table 4. List of Prominent Countries in MP Research (Based on Corresponding Authors Countries).

Country Articles SCP MCP Freq MCP_Ratio

United Kindgom 109 102 7 0.147 0.064
USA 83 78 5 0.112 0.06
Australia 56 55 1 0.075 0.018
China 30 26 4 0.04 0.133
Germany 21 21 0 0.028 0
Canada 18 17 1 0.024 0.056
Spain 16 15 1 0.022 0.063
Italy 11 10 1 0.015 0.091
Sweden 11 11 0 0.015 0
Note: SCP, single-country publications (the number of publications in which all the authors are from a single country);
MCP, multiple-country publications (the number of publications with authors from more than one country. This indicates
international collaboration and the global engagement of researchers.); Freq, frequency (the number of times a specific
country appears as a corresponding author’s country in the research publications); MCP_Ratio, multiple-country publications
ratio (the proportion of publications with international collaboration relative to the total number of publications from that
country. This ratio highlights the extent of a country’s involvement in global research networks compared to its overall
research output.).
10 Journal of Creative Communications

Figure 4. Authors and Country Collaboration Map.

Note: The light blue colour signifies fewer publications; dark blue denotes a high volume of publications. The connecting lines
indicate the collaboration. The thicker line refers to strong and significant collaborations.

Germany (21), Canada (18), Spain (16), Italy (11) and Sweden (11). From the authors and country
collaboration map (Figure 4), it can be observed that there is a global collaboration. The different shades
of blue portray the collaboration among various countries. The line that links countries indicates the
extended collaboration. This information is crucial when it differentiates between single-country
publications, which involve collaboration within a country, and multiple-country publications, having
inter-country collaborations. It is worth noting that the University of Notre Dame and Stanford University
appear to be significant contributors to MP. This map indicates significant collaboration between the
United Kingdom and the United States. They have a profound influence on MP that is reciprocal. Artists
from both countries are borrowing elements from each other’s musical traditions. Even music producers
and sound engineers from both countries share their expertise for the success of artists and MP on
both sides.

Intellectual Structure Evolution: Thematic Growth and Development

Science mapping, using bibliometric techniques, elucidates emerging topics in specialised fields.
Thematic and network analyses provide insights into both the bibliometric structure and the intellectual
landscape of the research domain (Donthu et al., 2021). Thematic analysis is employed to quantify and
visually represent the evolution of themes within the specific domains in a two-dimensional way. This
approach incorporates various bibliometric measures but is not limited to the h-index. The themes
identified in this study and the thematic areas detected in the dataset are used to measure the impact of
the specified domain. The author’s keywords are selected as a unit of analysis with a minimum cluster
Thomas and Kumar 11

Figure 5. Thematic Map.

frequency of 10 occurrences. Figure 5 showcases the diverse themes that have emerged from the thematic
analysis of corpus in MP.
Thematic analysis has different stages. For analysis, the MP is sliced into four stages. The upper
right quadrant, named ‘Motor Themes’ as suggested by Cobo et al. (2011) and Aria et al. (2022), is
fundamental to the field of MP. The emergence of themes such as music, creativity, the relevance of
AI—music generation, deep learning and computational creativity in MP is notable. Scholars are
showing a growing inclination towards AI and technology that helps in MP. The upper left quadrant
is named the ‘Niche Themes’ comprising themes of marginal importance to the field (Cobo et al.,
2011). These themes have high internal relations. It is interesting to note the representation of
Czechoslovakia in the niche theme. Czechoslovakia’s cultural and stylistic influence shaped certain
aspects of music. Given Czechoslovakia’s rich history in classical and folk music, composers and
musicians have contributed to the global music ecosystem, particularly in genres that draw on
Eastern European sounds or sociopolitical themes. The emerging cluster that lies on the upper right
to centrality shows the fusion of classical and Western music composition resulting in hybrid genres
like classical hip-hop or hip-hop era. These intersections push the boundaries of what is tradition-
ally accepted in both genres. The themes situated in the lower left quadrant are regarded as mar-
ginal. They are either emerging or declining in the domain. In this case, three clusters are residing
almost to centrality where one cluster is on the margin, and others are a little bit away from the
margin. Margin refers to the centrality map or conceptual space. Although these clusters are not
common, they receive more attention due to the application of the latest technology. The lower right
quadrant consists of themes that are not highly developed but are fundamental to the main field or
domain of MP. Even though they are general and transversal, they are needed for a better under-
standing of domains such as music improvisation, algorithmic composition, electronic and interac-
tive music generation, neural networks, cultural identity and the music industry, which lead to a
better understanding of motor themes.
12 Journal of Creative Communications

Figure 6. Thematic Evolution.

The concept of keyword dynamics serves as a valuable lens through which the observation of the
theme evolution and the specific domain are identified. The cumulative frequency of the keywords
between 1977 and 2023 (Figure 6) like ‘Music’, ‘Music Production’, ‘Music Technology’, ‘Music
System’ and ‘Creativity’ have high impacts on both the word cloud (Figure 7) and graph (Figure 8).
Keywords such as ‘Music Production’, ‘Music Technology’, ‘Creativity’ and ‘AI Music’ have increased
between 2001 and 2023. The music has been growing on par with technology and creativity. Music pre-
viously experienced high frequency and rapid evolution, but the scenario has since shifted, with MP
transforming dramatically due to technological advancements.

Grouping Themes and Describing Clusters: A Thematic Analysis

The VOSviewer version 1.6.19 was used for conducting a comprehensive analysis and visualisation
between keywords. Keyword co-occurrence analysis is used to understand the knowledge base
particularly the association amongst keywords in a certain domain. Two important terms need to be
mentioned before the results are interpreted. The network visualisation mainly contains colour-coded
clusters. Each cluster has nodes that represent keywords that have common attributes. These nodes are
linked by ‘curved lines’ that show the strength of the relationship between each keyword. It has been
accepted that the network of co-occurrences offers researchers insights into understanding the components
and structure of a specific domain (Figure 9). The network visualisation of keyword co-occurrence is
Thomas and Kumar 13

Figure 7. Word Cloud of Prominent Keywords.

Figure 8. Frequency of Keywords Between 2001 and 2023.

displayed at 2,386, of which 13 met the threshold value. The selection was based on the inclusion criteria
of a minimum of 10 co-occurrences. Table 5 is a mapping of four different clusters.
The keyword co-occurrence network graph reveals four distinct clusters, each represented by differ-
ent colours. The nodes and linkages in red constitute the first cluster, which includes keywords such as
AI, creativity, deep learning and music generation. This cluster focuses on technological perspective
focusing on advancements in recording media and digital tools, highlighting the role of technology in the
creative process. Therefore, it can be identified as a technological perspective. The second cluster, shown
14 Journal of Creative Communications

Figure 9. Keyword Co-occurrence Network Analysis Map.

Table 5. Frequency of Keywords and Their Respective Clusters.

Cluster 01 Cluster 02 Cluster 03 Cluster 04

Artificial Intelligence (10) Gender (13) Music Education (14) Human (16)
Creativity (28) Music Industry (18) Music Technology (21) Music (88)
Deep Learning (12) Music Generation (16) Technology (10)
Music Genre (14) Music Production (66)

in green, includes keywords such as music industry, music generation, gender and MP. This cluster is
associated with the broader context of the social and cultural intersection of MP, so we named it the
social and cultural perspective. The third cluster, depicted in blue, comprises keywords such as music
education, music technology and technology. This cluster represents the ongoing industrial advance-
ments in the field of music and explores economic, organisational and commercial aspects, including
industry changes and the evolving roles of music producers. So, we named it the industrial perspective.
Finally, the fourth cluster, represented in yellow, includes keywords such as human and music. It is basi-
cally how music affects humans. It also highlights creative and artistic dimensions, emphasising human
agency and musical creativity, so we termed it the humanistic perspective. An important insight from this
cluster is that it seems to encapsulate elements from the previous clusters, indicating interconnections
among different keywords. This cluster signifies the relationships and overlaps between the human
Thomas and Kumar 15

aspect of music and the technological advancements identified in other clusters. These four clusters
were identified based on the co-occurrence of keywords in the network graph, offering four distinct
perspectives.

Significant Contributions and Future Research Directions

We utilised co-citation analysis to corroborate and classify the clusters. The co-citation network refers to
the author with at least two citations who are cited together in scholastic publications. With this, the
network identified four clusters that highlight the conceptual development in MP. Influential publications
were identified using the citation score for each cluster. The first 10 articles in each cluster were read
carefully to unearth the commonalities and differences in the clusters. A total of 40 articles were reviewed
at length to explain the clusters. Based on our analysis of the selected literature, we have done an in-depth
analysis of MP. This comprehensive work provides a nuanced understanding of MP, offering a foundation
for future research and industry analysis.

Humanistic Perspective
In this approach, the authors explore both the process and outcomes of music’s influence on humans.
Music profoundly impacts the human mind, emotions (Lundqvist et al., 2008) and well-being, influencing
both physiological and psychological aspects. Listening to music releases dopamine in the brain,
enhancing mood and creating pleasure (Kayser, 2014). It can evoke memories and strong emotional
responses such as joy, sadness or nostalgia (Davies, 2010) and improve cognitive processes such as
concentration and productivity (Rickard et al., 2005). This underscores music’s transformative power in
shaping thoughts, feelings and behaviours (Duman, 2023).
Historically, music has been seen as a mystical power reducing stress since ancient Greece (Juslin &
Sloboda, 2013). In religious contexts, Sufis noted its therapeutic effects on mental and neurological dis-
eases (Özeke, 2009). For infants, a mother’s heartbeat is like music, providing peace and spiritual ben-
efits (Campbell & Scott-Kassner, 2002). Music improves quality of life (Ruud, 1997) and shows a causal
relationship between musical training and cognitive improvement (Hao et al., 2023). It can reduce juve-
nile crime (Chong & Yun, 2020), impact neurophysiological systems (Rodriguez et al., 2021) and thus
provide relaxation (Adiasto et al., 2022). Active music participation offers physical, psychological and
social benefits (Dingle et al., 2021), and engaging in musical activities aids anti-ageing (Abrahan et al.,
2019). These aspects align with the uses and gratifications theory, where users gain both tangible and
intangible benefits.
Effects of Background Music on Learning (Research Gap)
Background music plays a significant role in various contexts, using tempo, rhythm, volume, harmony
and melody to elicit moods and elevate the emotions of the audience or listeners (Jaspers, 1991). On
social media platforms like YouTube, it enhances appeal (Kuo et al., 2013); in video games, it aids
concentration and evokes emotions (Linek et al., 2011). Instrumental background music can improve
workplace attention (Shih et al., 2012). College students use music for concentration while studying as
a mood regulator (Greasley & Lamont, 2011), and for relaxation while driving (Dibben & Williamson,
2007). Most studies focus on the influence of background music on simple cognitive tasks, such as the
retention of characters, words, images or facts, with fewer studies examining its impact on complex
cognitive tasks, such as applying, analysing, creating or evaluating knowledge.
16 Journal of Creative Communications

Potential Negative Effects of Music Consumption (Research Gap)

Negative effects of music include hearing damage (Hanson & Fearn, 1975), reduced work performance
from excessive listening (Chen et al., 2023) and inappropriate content (Wang et al., 2022). Some genre
like electronic dance music is linked to higher anxiety and depression (Linto et al., 2024; Palamar
et al., 2019), sleep pattern issues causing fatigue and health complications (Johnson & Richert, 2014),
negative behavioural attitudes (Maxwell, 2005), and increased alcohol and drug use among teenagers
(Farrugia & Fraser, 2017). Numerous studies highlight the association between music and risky
behaviours, yet limited research exists in developing nations (Linto et al., 2024), indicating a significant
knowledge gap.

Technological Perspective
Music is a powerful form of human expression and has been revolutionised by technological advance-
ments. Roger’s diffusion of innovation theory aligns with technological developments in music. AI tech-
nology has significantly impacted the music industry. Briot and Pachet (2018) highlighted numerous
studies on AI processes such as machine learning, deep learning and natural language processing. While
AI in MP is still developing, it offers potential for AI interpolation between musical materials and syn-
thesising complete multi-instrumental tracks, gradually transforming the industry by providing musi-
cians with powerful creative and efficient tools.
Human-composed Music Versus AI-composed Music (Research Gap)
Defined as the ‘automation of human behaviour’, AI has shown its ability to produce high-quality output
(Despautz, 1994; Frey & Osborne, 2017; Roads, 1985). Comparative studies between AI-generated and
human-created music raise questions about their relative merits. AI-generated music offers ease of
creation without the need for extensive training, skills or expensive equipment. However, it also sparks
debates about the artist’s role and the intrinsic value of human creativity in MP and the industry. This
exploration contributes to the dialogue on the evolving relationship between technology and artistic
expression.

Social and Cultural Perspective

Music plays a significant role in shaping and advancing social and cultural aspects of human societies.
It serves as a powerful medium of expression, communication and identity formation, influencing
individuals and communities across the globe. Music significantly shapes social and cultural aspects,
serving as a medium for expression, communication and identity formation. It influences individual and
collective identities related to gender, ethnicity and nationality (Herbst, 2019). Music is integral to social
movements, political expression, protest and social change. Its ritualistic and symbolic roles in ceremonies
and religious practices highlight its cultural importance. Music reflects and shapes the identity of places
and people, influenced by local and regional contexts. Live music enriches cultural environments
(Hudson, 2006; Linto et al., 2024; Martin, 2017; Wynn, 2020), though its social and cultural values are
often overshadowed by economic factors (Cloonan, 2011; Martin, 2017). Modern urban culture and
events like the Sunburn Festival are rising, creating job opportunities and developing the music industry
through a network of venues, festivals and social actors (Behr et al., 2016), which shape live music
performances and MP overall.
Thomas and Kumar 17

Nature-inspired Music (Research Gap)

Existing studies primarily explore integrating nature sounds with traditional or electronic instruments,
often using synthesised tools or digital processing (Jensenius, 2022; Selfridge & Pauletto, 2022).
However, there is a gap in the scholarly landscape regarding music created exclusively from natural
sounds without instruments such as keyboards, MIDI or DAWs. While nature-inspired music is studied,
a comprehensive exploration of crafting music solely from unaltered natural sounds—considering
artistic, technical and cultural aspects—is notably lacking.

Industrial Perspective
Recent advancements in the music industry have been driven by digital and computer programming
technologies, including AI, voice control, machine learning and blockchain (Darvish & Bick, 2023).
These innovations have revolutionised music creation, production and distribution. Digital tools and
software have democratised production, allowing artists to create professional-quality music from home.
Cloud-based platforms like Splice have enhanced collaboration and accessibility, particularly during
the COVID-19 pandemic (Arrieta, 2021). Streaming services use AI and data analytics for personalised
experiences, with Spotify’s Application Programming Interface facilitating advanced content manage-
ment (Raffa, 2024). However, challenges include economic model complexities (Kossecki et al., 2021),
infrastructure demands (Ofochebe, 2020) and impacts on artist success (Patokos, 2008). Legal issues
such as copyright and intellectual property concerns (Albinsson, 2013; Nyathi & Maguraushe, 2023;
Tschmuck, 2009), as well as globalisation’s effects on cultural diversity (Throsby, 2002) also arise. The
digital shift necessitates specialised education (Rempel et al., 2013) and raises questions about technol-
ogy’s influence on creativity (Katz, 2010). This transformation has reshaped the independent music
sector, democratising production and distribution.
Algorithm and MP (Research Gap)
Since technology is a boon for the advancement of any domain, the MP and music industry has started
capitalising on computer-assisted and programming-based tools. Although the use of AI technology for
MP is still in its infancy, it has the potential to make a lasting impact on the way MP is done. Generative
AI has been a dominating type of AI that has created new opportunities as well as caused unexpected
challenges (Novikova, 2024). Thus, the contribution of AI, more specifically generative AI, and how its
implementation in the MP and music industry should be studied. Furthermore, usage patterns, as well as
issues and challenges that arise in the practical use of AI-based tools, should be studied to supplement
the industry needs and recommend practical implications.
Women in the Music Industry (Research Gap)
Music and technology fields, often perceived as male-dominated, include roles such as audio engineers,
sound designers and film sound recordists. Historically, women were under-represented, but their
involvement has gradually increased. Notable female figures include electronic musicians such as Laurie
Anderson, Alice Shields and Amy X Neuberg (Gaston-Bird, 2019), and sound engineers such as Leslie
Ann Jones and Christina C. Miserendino (Strong & Raine, 2019). Innovators like Anne-Marie Bruneau
in loudspeaker design, Ingrid Linn in audio plug-ins and Poppy Crum at Dolby Laboratories have made
significant contributions. Marina Bosi has developed standards for audio and video coding. Despite
progress, gender disparities persist, particularly in developing countries like India, where women face
under-representation and challenges in the MP industry. The knowledge gap exists in understanding the
18 Journal of Creative Communications

women workforce in MP in India, highlighting the need for solutions to foster gender equality and
address challenges and opportunities presented by digital platforms.

Conclusion
This comprehensive study traces the evolution of MP from analogue to algorithmic techniques, employing
a methodical analysis of influential authors, journals, countries and intellectual themes. The research
identifies four key perspectives in MP: Humanistic, technological, social and cultural, and industrial,
providing a nuanced understanding of the field’s complexity. The integration of AI into MP has yielded
both positive and negative outcomes, revolutionising music creation, consumption and discovery while
raising concerns about the potential loss of human artistic expression (Seaver, 2017). The study highlights
significant research gaps, particularly in understanding the ethical implications and synergy between AI
and human creativity in MP. As the industry continues to evolve, this research emphasises the need for a
balanced approach that leverages technological advancements while preserving the essence of human
artistry. Future research directions should focus on exploring the ethical, cultural and creative dimensions
of AI in music, contributing to both scholarly discourse and industry practices in navigating this
transformative era in MP.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest concerning research, authorship and/or publication of this
article.

Funding
The authors received no financial support for the research, authorship and /or publication of this article.

ORCID iD
V. Vijay Kumar https://orcid.org/0000-0002-2137-0102

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Authors’ Bio-sketch
Linto Thomas is a doctoral research scholar at the School of Communications, XIM University,
Bhubaneswar, Odisha, India. His research area is AI-based music production. He worked as a sound
engineer and music composer at Chetana Sound Studios and an instructor for sound-related courses at
Chetan Media College, Thrissur, Kerala, India. At XIM University, he handles courses on computer
graphics, audio engineering and audiovisual production. He is a YouTuber and a sports enthusiast.

V. Vijay Kumar, PhD, is an associate professor and dean of the School of Communications, XIM
University, Bhubaneswar, Odisha, India. He is a seasoned mass communication professional and a
media educator, with ample years of industrial experience in audiovisual content development, super-
vision and management. Right from television reality shows to non-fiction live broadcasts, from tele-
vision commercials to feature films, he worked on various projects in different capacities. He worked
with Sun TV Network, Chennai; Shop CJ TV Network, Mumbai; Frames Entertainment, Chennai,
India, in senior roles, and he worked as a creative head and show director of an award-winning Tamil
reality talent hunt show Naalaya Iyakunar [transl. Future Director] and South India’s first reality tele-
vision show for identical twins Iruvar [transl. Twins]. He completed his PhD from Anna University,
Chennai, India. His doctoral research is on the topic ‘Interactive Reality Television’. He specialises in
audiovisual content development, entertainment television programmes, digital film-making and edu-
cational media design. His research interests are television programming, television studies, film stud-
ies, Tamil cinema, social media, interactive digital communication, positive psychology, public
relations, corporate social responsibility and educational media. Apart from teaching, he is actively
involved in audiovisual content development in the forms of documentaries, music videos, television
shows, short films and educational video modules in the capacity of executive producer, head of
creatives and production.

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