Music Production Research Articles

Hip-hop music producers’ labour in the digital music economy: Self-promotion, social media and platform gatekeeping

There has been much debate concerning the changing nature of cultural production and distribution in the digital creative economy. Music production work has been especially affected by promotional conventions established by social media and music streaming platforms. This article critically builds atop perspectives on the platformisation of cultural production to investigate how independent hip-hop music producers develop their careers in the era of digital media platforms. It examines how traditional media and digital platform gatekeepers affect producers’ abilities to professionalise, promote creative work to audiences and manage precarious conditions for their labour. Insights from interviews with 15 producers from 8 countries are analysed and discussed to provide a nuanced view of the conditions for music production careers in the platform era of the digital creative industries.

Engaged Amazigh Poetry in Meteor Airlines' "Agdal": Reviving Tradition while Addressing Global Environmental Issues

This paper analyzes Meteor Airlines' 2024 album Agdal, which revitalizes traditional Amazigh poetry through the emerging genre of Amazigh Rock. The study aims to explore how Agdal integrates the cultural heritage of pre-Saharan Amazigh oasis communities into global environmental discussions, with an emphasis on preserving nomadic and pastoral traditions. Using a qualitative analysis of the album’s lyrics, music style, and its reception, this research examines how the album marks a shift in Morocco's cultural industry from commercialized music production to efforts centered on cultural preservation and archiving. Findings suggest that Agdal, by merging traditional Amazigh poetry with contemporary rock elements, engages transnational audiences and contributes to an ongoing global dialogue on ecological and cultural sustainability. The study concludes that contemporary Amazigh music plays a crucial role in preserving historical memory, fostering cultural resilience, and raising environmental awareness, illustrating the impact of local cultural production on wider global issues

The algorithmic nature of song-sequencing: statistical regularities in music albums

Based on a review of anecdotal beliefs, we explored statistical patterns of track-sequencing within a large set of released music albums. We found that songs with high levels of valence, energy and loudness are more likely to be positioned at the beginning of each album. We also found that transitions between consecutive tracks tend to alternate between increases and decreases of valence and energy. These findings were used to build a system which automates the process of album-sequencing. Our results and hypothesis have both practical and theoretical applications. Practically, sequencing regularities can be used to inform playlist generation systems. Theoretically, we show that professional musicians and music producers have significant levels of agreement about how to determine the order of tracks in their albums.

A Content Analysis Study of Distortion's Influence on Drum and Bass Music Production

This study uses content analysis and draws upon aesthetic theory and technological determinism as theoretical frameworks to explore contemporary approaches to the use of distortion within drum and bass production. To achieve this, it investigates online video tutorials created by professional drum and bass producers and online content creators in order to extract insights into their common working practice when using distortion in their work. The analysis highlights a prevalent use of aggressive distortion to transform bass timbres, which is a trend originating in the 1990s that continues to shape the genre's evolving aesthetic. This research provides unique insights into the evolution. and impact of distortion and shows how established techniques persist while subtly evolving within the ever-changing production ecosystem.

From Analogue to Algorithm: The Metamorphosis of Music Production Techniques—An Integrated Literature Review

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.

Alternative Imaginaries of Cyprus: Space and Narrative Re/construction in Local Music Video Production

The article offers a close reading of music videos created in the Republic of Cyprus during the past two decades, focusing specifically on the ways the audiovisual texts represent space and (re)construct narratives in/of the Greek Cypriot context. Building on existing work in the area of lens-based media studies, namely cinema and photography, the article firstly examines how music videos co-exist and converse with other types of audiovisual representations of the island of Cyprus. Secondly, it presents a classification of local music videos, based on how such creative outputs represent Cyprus as a locale. In doing so, it highlights the ways in which alternative visual treatments might, consciously or otherwise, speak back to and rewrite narratives associated with specific places. The videos are also examined in relation to the lyrical content of the songs and the music genres they belong to. Our work contributes to the expanding body of research on the popular music scape of the island, particularly concentrating on the transgressive dynamic of strands of the Greek Cypriot independent music scene. This is the first study that offers a close examination of local music video production, which we place in the growing scholarly debates about how recent artistic expression in the Republic of Cyprus destabilises dominant representations of space and identity and produces new aesthetics of the Cypriot experience.

The Role of Cultural Heritage in The Development of Tourism and Some Cultural Industries in Vietnam

Many nations now view the cultural industry as a significant role in the country's cultural life and as a vital economic sector supporting sustainable development. Vietnam has an affluent cultural history with diverse cultural identities and is valuable in every way. The development of various cultural industries benefits greatly from the resources provided by cultural heritages. In order to elucidate the significance of cultural heritage in the growth of particular cultural industries, such as music, film, and cultural tourism, we employ synthetic analysis techniques and field research in this article. According to the research findings, cultural heritages are becoming important resources for the growth of cultural tourism, giving travellers motivation to travel, learn, and carry out research. Cultural heritage also provides resources and inspiration for music and film production, meeting the public's needs for cultural entertainment. The article makes inferences about the value of cultural heritage; development constraints, and potential solutions for preserving and enriching cultural heritage within the cultural industry. It also provides ways to improve human resources in the fields of music, film, and cultural tourism.

Film Orchestral Music: Creative Synergy and Production

The purpose of the article is to substantiate the importance of orchestral music in the creation of film music content based on the definition of its synergistic properties. The research methodology is based on the principles of a system–synergistic approach (in determining the synergistic properties of orchestral music and the components of film music production), methods of analysis (in processing scientific sources on the topic), synthesis (in formulating the conceptual apparatus of the study) and generalisation (in determining the scientific novelty and results of the study). The scientific novelty of the results obtained is that the synergistic properties, functions, stages of creation and recording of orchestral music for film are determined and scientifically substantiated. Conclusions. Orchestral music is an integral and full-fledged part of a film work; it performs artistic and expressive, emotional and psychological, and hedonistic functions. When creating a high-quality film soundtrack, one should take into account the synergistic properties of orchestral music (openness, attractiveness, coherence, emergence). The success of orchestral film music production depends on the creative interaction of the participants in this process – composer, arranger, orchestra and conductor, sound engineer, music producer.

Pedagogical Approaches in Music and Audio Education for Deaf and Hard-of-Hearing Students

Learning about music, sound or audio can present significant challenges for individuals who are deaf and hard of hearing (DHH). Given the advancements in technology and the increasing emphasis on equality, diversity and inclusion (EDI) in education, this article proposes pedagogical approaches aimed at facilitating the learning process for DHH students in the areas of music and audio production. These approaches encompass sound visualisation, haptic feedback, automated transcription, tactics in non-linear editing and digital signal processing. Importantly, these approaches do not necessitate advanced technical skills or substantial additional resources, thus lowering barriers for DHH students to overcome challenges in music and audio production. Furthermore, these strategies would enable content creation and editing for individuals with DHH, who may have previously been excluded from participating in music and audio production. Recommendations are provided for the implementation of these approaches in diverse educational settings to promote the integration of EDI in music and audio education.

Revolutionizing Music Production with ChatGPT Applications: Accessibility, Creativity, and Impact on the Industry in China

This study investigates the revolutionization of the application of ChatGPT with respect to its accessibility, creativity, and impact on music industry in China. To achieve this, the study employed Z-test technique for difference in proportion and the ordered logistic regression model. These methods were used to explore deeper into suitable strategies using survey with a sample size of 385 music artists including music producers, songwriters, artists/musicians, and sound engineers.

Generative Adversarial Network Based Music Generation

Music has been an integral part of human civilization personally and culturally. Historically, music has been generated using various instruments, or natural sounds like water drops, or unconventional musical instruments like metal or glass-wares. At present, technologies like Musical Instrument Digital Interface (MIDI) are used to generate music electronically. This research investigates the use of Generative Adversarial Networks (GANs) for beginner-friendly music production. This model uses Long Short-Term Memory (LSTM) generator and Patch GAN as discriminator for the GAN architecture. The generator consists of input layer, embedding layer, LSTM layer and generates output with a SoftMax function. Similarly, the discriminator consists of a convolution layer, the output of which is averaged by the global average pooling layer, and output is generated by the sigmoid function. The model is trained on Maestro MIDI dataset. We make the process understandable by delving into the implementation specifics and outlining the fundamental concepts of music. Our effective model highlights the potential of GANs in music composition by producing cohesive music. After training for 50 epochs, the model exhibited a remarkable precision of 91.82 percent. This project uses the combination of Artificial Intelligence (AI) with music theory to provide intriguing new opportunities in the field of music. The model can be beneficial for different industries like gaming, music, entertainment, education, etc.

Playful Computation

This paper investigates students’ experiences and teachers’ attitudes towards playful computation: an innovative pedagogy that emphasizes playful learning to teach students information and computing technology. A pilot study was conducted at a Californian primary school during the summer, involving 84 students and 5 teachers engaging in creative and playful computing activities such as 3D printing, coding drones, redubbing audio, building computers, and music production. Student surveys, teacher interviews, and classroom observations were collected in mixed-methods research to provide multiple perspectives on the challenges and benefits of implementing the pedagogy. Key findings indicate that playful computation significantly boosted student engagement and enjoyment, even surpassing student expectations. Teachers also expressed surprise at the increase in engagement as well as persistence, attributing this to the intrinsically rewardingnature of playful activities. Playful computation also promoted student self-expression and collaborative learning. However, teachers expressed concerns about the practicality of implementing this pedagogy in standard educational settings due to existing structural constraints of aligning with academic standards. Classroom management and lack of established norms for play in this learning context also limited their lesson plans and implementations. Supporting the existing literature on the benefits of playful learning, this research also suggests the need for further investigation into its role in facilitating flow and non-cognitive traits like grit. Investigating how playful computation impacts students’ testable learning outcomes is also recommended as a necessary research direction to facilitate broader implementation in American classrooms.

Conjoint Analysis to Define Beer Label and the Influence of Labeled Bottles and Music on the Sensory Profile of Craft and Industrial Beers

ABSTRACTPackaging label and music influence product acceptance and sensory profiling. This study defined the ideal label for a craft beer using the modified choice‐based conjoint analysis (MCBCA), as well as the sensory profile of this craft beer and an industrial beer in two tests: a blind test and a test in the presence of the labeled bottle and of a sertanejo universitário song, using a rate‐all‐that‐apply (RATA) list. MCBCA revealed that craft beer label's most favorable attributes included information on harmonization with food, a traditional‐themed illustration, and a strong emphasis on the brand and beer type. The RATA list comprised descriptive attributes, affective terms, product‐relevant phrases, and feelings. Nonsensory characteristics had a positive impact on the craft beer description but a negative impact on the industrial beer, with descriptors such as “watery,” negative affective terms memories for it. The influence of labeled bottles and music on consumers' sensory descriptions underscores the importance of nonsensory characteristics in the product's evaluation and success.

END-TO-END AUTOMATIC MUSIC TRANSCRIPTION OF POLYPHONIC QANUN AND OUD MUSIC USING DEEP NEURAL NETWORK

This paper introduces an automatic music transcription model using Deep Neural Networks (DNNs), focusing on simulating the "trained ear" in music. It advances the field of signal processing and music technology, particularly in multi-instrument transcription involving traditional Turkish instruments, Qanun and Oud. Those instruments have unique timbral characteristics with early decay periods. The study involves generating basic combinations of multi-pitch datasets, training the DNN model on this data, and demonstrating its effectiveness in transcribing two-part compositions with high accuracy and F1 measures. The model's training involves understanding the fundamental characteristics of individual instruments, enabling it to identify and isolate complex patterns in mixed compositions. The primary goal is to empower the model to distinguish and analyze individual musical components, thereby enhancing applications in music production, audio engineering, and education

Music source feature extraction based on improved attention mechanism and phase feature

Music source feature extraction is an important research direction in music information retrieval and music recommendation system. To extract the features of music sources more effectively, the study introduces the jump attention mechanism and combines it with the convolutional attention module. Also, a feature extraction module based on Unet + + and spatial attention module is proposed. In addition, the phase feature information of the mixed music signals is utilized to improve the network performance. Results showed that this model was studied to perform well in music source separation experiments of vocals and accompaniment. For vocal separation on the MIR-1K dataset, the model achieves 11.25 dB, 17.34 dB, and 13.83 dB for each metric, respectively. Meanwhile, for drum separation on the DSD100 dataset, the model achieves a median signal-to-source distortion ratio of 4.36 dB, which is 2.91 dB better than that of the Spectral Hierarchical Network model. For the separation of the bass sound and the human voice, the model's in the separation of bass and human voice, the median distortion ratio of the model is as high as 4.87 dB and 6.09 dB, which is better than that of the Spectral Hierarchical Network model. This indicates the significant performance advantages in feature extraction and separation of music sources, and it has important application values in music production and speech recognition.

Becoming Reflective Practitioners in the Music Business

This case study is from the music business production where we have investigated how supervisors at music camps develop as reflective practitioners. We have investigated the process of how the supervisors gradually become aware of their tacit knowledge and are able to make it explicit, following the SECI-model through reflecting. It is also interesting to identify how they maintain a production focus throughout each process of conducting the music camps, but at the same time opens up for reflection and learning. As the lecturers also work at a music production company, and we have sought to unveil how becoming reflective practitioners have made a difference when it comes to executing their profession. Hence, trough qualitative investigations we have interviewed the supervisors and their co-workers in their organization to unveil how this has had an impact in their organization and contributed to enhanced organizational knowledge.

A Transformação da Produção Musical Independente de Canções no Século XXI

Independent record labels and their often implicit relationship with major labels are one of the themes investigated in this historical retrospective through exploratory research with bibliographic review that addresses the musical landscape in which independent artists are embedded. Due to the complexity and breadth of the topic of independent production in the music industry, it was chosen to explore not only the structure of independent record labels but also the nature of independent scenes themselves. For this purpose, categories such as "independent artist," "artist direct" (Mulligan, Mark, 2021), and "artist entrepreneur" (Cerqueira, 2018) will be addressed. Furthermore, the importance of the contemporary connection between independent artists and the consumer market for songs through social networks will be highlighted, as they have become an important channel for the dissemination and promotion of their works. This work arises from the close relationship of one of the authors of the article with independent music production, being himself a artist direct who seeks to understand the challenges and opportunities that permeate this ever-changing musical universe.

AI-empowered consumer behavior analysis for trustworthy track recommendation over musical dance electronic products

—In the digital music era, accurate and trustworthy track recommendations for musical dance electronic products are becoming increasingly important to improve user experiences and attract more consumers. Consumer behavior modeling is critical in user interest learning and has been extensively used in recommender systems to improve recommendation accuracy. This paper proposes a novel AI-empowered consumer behavior analysis method for trustworthy track recommendations over musical dance electronic products. Specifically, we first model consumer behavior by integrating collaborative filtering and a hidden Markov model to capture the key interactive patterns between consumers and musical dance electronic products. Then, we develop a trustworthy track recommendation method based on multi-layer attention representation learning, which leverages scattering transform for audio preprocessing and attention-based independent recurrent neural networks for encoding user preferences and product features. Extensive experiments on real-world datasets demonstrate the superiority of our proposed method in terms of recommendation accuracy and trustworthiness.

Israel's Zangwill; or, The Return of the King of Schnorrers

Abstract This article explores Israel Zangwill's posthumous presence in Israeli culture, as reflected in various media and discursive arenas: press coverage of his death and Yahrzeits; trends in the translation, publication and staging of his works; the inauguration of streets bearing his name; and references to his views and legacy in various political debates. Demonstrating how the tensions and contradictions so typical of Zangwill's persona were interpreted by cultural commentators or appropriated by opposing political camps, the first part of the article traces and contextualises Zangwill's gradual disappearance from the Israeli cultural mainstream. The second part then moves on to consider Zangwill's unexpected comeback in 2021, when a musical production of The King of Schnorrers, adapted by Nati Brooks, was staged in Tel Aviv. While the renewed interest in Zangwill's work is rooted specifically in the playwright's Anglo-Jewish background, the production employs Zangwill's 1790s Jewish London to consider ethnic tensions in present-day Israel.

Evaluating Feature Selection Algorithms for Machine Learning-Based Musical Instrument Identification in Monophonic Recordings

Musical instrument identification (MII) research has been studied as a subfield of the Music Information Retrieval (MIR) field. Conventional MII models are developed based on hierarchical models representing musical instrument families. However, for MII models to be used in the field of music production, they should be developed based on the arrangement-based functions of instruments in musical styles rather than these hierarchical models. This study investigates how the performance of machine learning based classification algorithms for Guitar, Bass guitar and Drum classes changes with different feature selection algorithms, considering a popular music production scenario. To determine the effect of feature statistics on model performance, Minimum Redundancy Maximum Relevance (mRMR), Chi-sqaure (Chi2), ReliefF, Analysis of Variance (ANOVA) and Kruskal Wallis feature selection algorithms were used. In the end, the neural network algorithm with wide hyperparameters (WNN) achieved the best classification accuracy (91.4%) when using the first 20 statistics suggested by the mRMR and ReliefF feature selection algorithms.