The ongoing COVID-19 pandemic has affected countless businesses, leading to serious disruptions for many industries. Drawing on the resilience literature, this study offers an understanding of the impact of the COVID-19 pandemic on the creative industries and their response to the challenges they have encountered. This study reviews 59 papers following the systematic literature review approach and reveals several positive implications of the COVID-19 pandemic within the creative industries (e.g., IT and software) as well as the negative (the music industry, festivals, cultural events). Identifying six themes related to the impact of the COVID-19 pandemic on the creative industries, we develop a response matrix based on the discussion of firms’ digital capabilities and their ability to adapt to the COVID-19 crisis. We outline future research directions using a Theory-Context-Characteristics-Methodology (TCCM) framework.
Danis Sugiyanto, Denik Iswardani Witarti, Komarudin Subekti
et al.
This research examines keroncong music as a representation of Javanese culture. The aim of the research was to interpret the lyrics of keroncong in a cultural context using thick description, Clifford Geertz. Keroncong music is also seen from a cultural diplomacy perspective. Research data was obtained from interviews and direct observation and supported by relevant literature. This research explicitly analyzes canonical songs such as Bengawan Solo, Tirtonadi, Taman Jurug, and Kota Solo to understand how cultural symbols are produced and consumed. Research data were obtained through participant observation, in-depth interviews, and focus group discussions (FGD) with a community of practitioners. Lyric analysis is integrated with High-Context Communication (HCC) theory to demonstrate how the indirect communication patterns of Javanese society are represented in keroncong musicality. The research found Javanese cultural symbols in the lyrics of keroncong songs. It highlights the city of Solo as the center of cultural civilization on the island of Java. Solo is known as “the spirit of Java.” The keroncong lyrics, which tell stories about people's lives, are able to attract and inspire love for Indonesia. Indonesian cultural diplomacy is supported by a community of keroncong music lovers abroad who act as diplomatic agents. The beauty of keroncong's musicality and poetic lyrics becomes a cultural signpost that is considered a soft power in cultural diplomacy. The conclusion of this research confirms that the rich culture of the Indonesian nation can be developed as an important instrument of cultural diplomacy. The success of cultural diplomacy requires direct contact with communities of Indonesian arts and culture lovers, at home and abroad.
Keywords: Cultural diplomacy, keroncong, keroncong, soft power, thick description
ABSTRAK
Penelitian ini mengkaji musik keroncong sebagai representasi budaya Jawa. Tujuan penelitian dilakukan untuk menerjemahkan lagu-lagu keroncong dalam konteks budaya dengan menggunakan thick description, Clifford Geertz. Musik keroncong juga dilihat dari perspektif diplomasi budaya. Penelitian ini secara eksplisit menganalisis lagu-lagu kanonik seperti Bengawan Solo, Tirtonadi, Taman Jurug, dan Kota Solo untuk memahami bagaimana simbol-simbol budaya diproduksi dan dikonsumsi. Data penelitian diperoleh melalui observasi partisipan, wawancara mendalam, dan diskusi kelompok terfokus (FGD) dengan komunitas praktisi. Analisis lirik diintegrasikan dengan teori High-Context Communication (HCC) untuk menunjukkan bagaimana pola komunikasi tidak langsung masyarakat Jawa direpresentasikan dalam musikalitas keroncong. Data penelitian diperoleh dari hasil wawancara, observasi langsung, dan didukung pustaka yang relevan. Penelitian menemukan simbol-simbol budaya Jawa dalam lirik lagu keroncong. Hasil analisis terhadap lagu-lagu keroncong dalam penelitian ini menonjolkan kota Solo sebagai pusat peradaban budaya di pulau Jawa, dikenal dengan istilah “the spirit of Java.” Lirik lagu keroncong yang bercerita tentang kehidupan masyarakat mampu menarik dan menimbulkan kecintaan terhadap Indonesia. Diplomasi budaya Indonesia didukung oleh adanya komunitas pecinta musik keroncong di luar negeri yang berperan sebagai agen diplomasi. Keindahan musikalitas keroncong dan lirik yang puitis menjadi soft power dalam diplomasi budaya. Kesimpulan penelitian ini menguatkan bahwa kekayaan budaya bangsa Indonesia dapat dikembangkan sebagai instrumen penting diplomasi budaya. Kesuksesan pelaksanaan diplomasi budaya memerlukan jalinan langsung dengan para komunitas pecinta seni dan budaya Indonesia, di dalam dan luar negeri.
Kata Kunci: Diplomasi budaya, keroncong, soft power, thick description
Muriel T. Zaatar, Kenda Alhakim, Mohammad Enayeh
et al.
Music is a universal language that can elicit profound emotional and cognitive responses. In this literature review, we explore the intricate relationship between music and the brain, from how it is decoded by the nervous system to its therapeutic potential in various disorders. Music engages a diverse network of brain regions and circuits, including sensory-motor processing, cognitive, memory, and emotional components. Music-induced brain network oscillations occur in specific frequency bands, and listening to one's preferred music can grant easier access to these brain functions. Moreover, music training can bring about structural and functional changes in the brain, and studies have shown its positive effects on social bonding, cognitive abilities, and language processing. We also discuss how music therapy can be used to retrain impaired brain circuits in different disorders. Understanding how music affects the brain can open up new avenues for music-based interventions in healthcare, education, and wellbeing.
Generative audio models typically focus their applications in music and speech generation, with recent models having human-like quality in their audio output. This paper conducts a systematic literature review of 884 papers in the area of generative audio models in order to both quantify the degree to which researchers in the field are considering potential negative impacts and identify the types of ethical implications researchers in this area need to consider. Though 65% of generative audio research papers note positive potential impacts of their work, less than 10% discuss any negative impacts. This jarringly small percentage of papers considering negative impact is particularly worrying because the issues brought to light by the few papers doing so are raising serious ethical implications and concerns relevant to the broader field such as the potential for fraud, deep-fakes, and copyright infringement. By quantifying this lack of ethical consideration in generative audio research and identifying key areas of potential harm, this paper lays the groundwork for future work in the field at a critical point in time in order to guide more conscientious research as this field progresses.
INTRODUCTION The global alcohol industry sponsors social/music events targeting young people; however, existing literature focuses on Westernised contexts. Given the decline in young people's drinking in many Western countries, it appears that multinational alcohol companies are importing the strategies they have used in high-income countries to the Global South countries like Nigeria to recoup profits. This study aims to examine the Guinness Show- a free one-month annual music festival, alcohol marketing at the festival and the extent to which the event encourages diverse drinking practices among its attendees. METHODS We observed the music festival before collecting data through 53 interviews and 3 focus groups (N = 26). Data were analysed to generate themes with the aid of NVivo 12 software. FINDINGS Over 6000 participants attend the Guinness Show daily, and participants gave detailed descriptions of the music festival, alcohol marketing activities that occur in it and the drinking practices of attendees, indicating that they were highly knowledgeable of the event. The Guinness Show attracts famous music artistes and other entertainers. Therefore, young people attend to see them perform free of charge. However, diverse alcohol promotions (e.g., quantity deals, low prices, giveaways) that happen daily, the strategic use of young women as 'beer promoters', and the pleasure the event induces by fusing music/entertainment into alcogenic environments, encourage drinking and drunkenness. All the attendees drank alcohol, and some engaged in impulse buying, while many consumed excessively due to promotions (e.g., buy-two-get-one free), which facilitated intoxication and the loss of control. CONCLUSIONS Guinness Nigeria organises the event for strategic brand communication, generating brand capital, and encouraging alcohol purchases and consumption among young people. Policymakers should reconsider self-regulation and implement national alcohol control policies and other public health interventions to restrain the alcohol industry from sponsoring such events.
Abstract Artificial Intelligence (AI) technology can reshape music education to enable students to learn musical instruments and compose music in a fun and easy way. This article examines the effectiveness of AI-enabled music teaching. The study seeks to incorporate AI-powered chatbots into piano classes in seven music schools and evaluate their effect on student performance. The results show that students who used the given app achieved better results than those enrolled in conventional classes. Overall, exploiting AI resulted in a 15% increase in academic performance, but the intensity of training led to variation in achievements. With this app, the scores increased as follows: piano playing by 6.51%; solfeggio and music literature by 4%; and vocal signing by 0.56%. The integration of chatbots in music education opens new developmental prospects. Based on the present findings, AI can be successfully integrated into music education.
Music training is generally assumed to improve perceptual and cognitive abilities. Although correlational data highlight positive associations, experimental results are inconclusive, raising questions about causality. Does music training have far-transfer effects, or do preexisting factors determine who takes music lessons? All behavior reflects genetic and environmental influences, but differences in emphasis-nature versus nurture-have been a source of tension throughout the history of psychology. After reviewing the recent literature, we conclude that the evidence that music training causes nonmusical benefits is weak or nonexistent, and that researchers routinely overemphasize contributions from experience while neglecting those from nature. The literature is also largely exploratory rather than theory driven. It fails to explain mechanistically how music-training effects could occur and ignores evidence that far transfer is rare. Instead of focusing on elusive perceptual or cognitive benefits, we argue that it is more fruitful to examine the social-emotional effects of engaging with music, particularly in groups, and that music-based interventions may be effective mainly for clinical or atypical populations. Expected final online publication date for the Annual Review of Psychology, Volume 75 is January 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Satvik Venkatesh, Arthur Benilov, Philip Coleman
et al.
There have been significant advances in deep learning for music demixing in recent years. However, there has been little attention given to how these neural networks can be adapted for real-time low-latency applications, which could be helpful for hearing aids, remixing audio streams and live shows. In this paper, we investigate the various challenges involved in adapting current demixing models in the literature for this use case. Subsequently, inspired by the Hybrid Demucs architecture, we propose the Hybrid Spectrogram Time-domain Audio Separation Network (HS-TasNet), which utilises the advantages of spectral and waveform domains. For a latency of 23 ms, the HS-TasNet obtains an overall signal-to-distortion ratio (SDR) of 4.65 on the MusDB test set, and increases to 5.55 with additional training data. These results demonstrate the potential of efficient demixing for real-time low-latency music applications.
Music generated by deep learning methods often suffers from a lack of coherence and long-term organization. Yet, multi-scale hierarchical structure is a distinctive feature of music signals. To leverage this information, we propose a structure-informed positional encoding framework for music generation with Transformers. We design three variants in terms of absolute, relative and non-stationary positional information. We comprehensively test them on two symbolic music generation tasks: next-timestep prediction and accompaniment generation. As a comparison, we choose multiple baselines from the literature and demonstrate the merits of our methods using several musically-motivated evaluation metrics. In particular, our methods improve the melodic and structural consistency of the generated pieces.
Modern music streaming services are heavily based on recommendation engines to serve content to users. Sequential recommendation—continuously providing new items within a single session in a contextually coherent manner—has been an emerging topic in current literature. User feedback—a positive or negative response to the item presented—is used to drive content recommendations by learning user preferences. We extend this idea to session-based recommendation to provide context-coherent music recommendations by modelling negative user feedback, i.e., skips, in the loss function. We propose a sequence-aware contrastive sub-task to structure item embeddings in session-based music recommendation, such that true next-positive items (ignoring skipped items) are structured closer in the session embedding space, while skipped tracks are structured farther away from all items in the session. This directly affects item rankings using a K-nearest-neighbors search for next-item recommendations, while also promoting the rank of the true next item. Experiments incorporating this task into SoTA methods for sequential item recommendation show consistent performance gains in terms of next-item hit rate, item ranking, and skip down-ranking on three music recommendation datasets, strongly benefiting from the increasing presence of user feedback.
Extensive literature spanning psychology, sociology, and musicology has sought to understand the motivations for why people listen to music, including both individually and socially motivated reasons. Music's social functions, while present throughout the world, may be particularly important in collectivist societies, but music recommender systems generally target individualistic functions of music listening. In this study, we explore how a recommender system focused on social motivations for music listening might work by addressing a particular motivation: the desire to listen to music that is trending in one’s community. We frame a recommendation task suited to this desire and propose a corresponding evaluation metric to address the timeliness of recommendations. Using listening data from Spotify, we construct a simple, heuristic-based approach to introduce and explore this recommendation task. Analyzing the effectiveness of this approach, we discuss what we believe is an overlooked trade-off between the precision and timeliness of recommendations, as well as considerations for modeling users' musical communities. Finally, we highlight key cultural differences in the effectiveness of this approach, underscoring the importance of incorporating a diverse cultural perspective in the development and evaluation of recommender systems.
Sakari Lahti, Tuomas Aaltonen, E. Rastorgueva-Foi
et al.
High-level synthesis (HLS) promises to increase the design and verification productivity for digital hardware systems. However, the industry still predominantly uses more time-consuming manual register-transfer level techniques instead of HLS. To accelerate the adoption of HLS, it is vital to explore if it is possible to achieve competitive results with this method. To that end, this paper demonstrates an HLS implementation of the well-known MUSIC algorithm for estimating the direction of arrival of a radio signal. We use as a receiver a four-antenna uniform linear array with one signal source and a resolution of one degree. For the computationally heavy eigenvalue decomposition within MUSIC, we employ the iterative Jacobi algorithm. We target two different Virtex FPGAs for synthesis and obtain results faring well in comparison to the previous literature, with $5.0\ \mu \mathrm{s}$ microseconds latency, high accuracy, and low resource consumption. The results show that HLS is suitable for implementing these kinds of algorithms on FPGA.
While the topic of listening context is widely studied in the literature of music recommender systems, the integration of regular user behavior is often omitted. In this paper, we propose PACE (PAttern-based user Consumption Embedding), a framework for building user embeddings that takes advantage of periodic listening behaviors. PACE leverages users’ multichannel time-series consumption patterns to build understandable user vectors. We believe the embeddings learned with PACE unveil much about the repetitive nature of user listening dynamics. By applying this framework on long-term user histories, we evaluate the embeddings through a predictive task of activities performed while listening to music. The validation task’s interest is two-fold, while it shows the relevance of our approach, it also offers an insightful way of understanding users’ musical consumption habits.
: The rapid integration of Artificial Intelligence (AI) in our daily lives has raised an always increasing attention for the need of developing AI literacy. In the realm of STEM (Science, Technology, Engineering, Mathematics), successful initiatives aiming to develop children’s AI literacy can be found in the literature. However, despite STEAM-based methods offer a great potential for music education too, the integration of the (A)rts in STEM education is mostly biased—the arts are used as a tool to support STEM subjects but not the other way around. With this background, the question how AI literacy might be promoted in the music classroom through audiovisual co-creativity and digital storytelling is explored in a music-pedagogy workshop with nine children (7 male, 2 female) aged 10 to 12. This work is a proof of concept that illustrates how generative tools might be used to support child-AI co-creative interactions and by this acquiring not only digital skills and knowledge but musical ones, too; thus, fostering an integrative development of artistic and technical competences.
A. Mezza, Riccardo Giampiccolo, Alberto Bernardini
et al.
Traditionally, drum source separation has been tack-led using nonnegative spectro-temporal factorization methods. Only recently, deep learning showed unprecedented performance in separating five stems from a drum mixture, namely, kick drum, snare drum, toms, hi-hat, and cymbals. The literature, however, still lacks a thorough comparison of the techniques readily available in the context of music source separation. In this paper, we conduct a first benchmarking analysis of music demixing models tailored for deep drum source separation. We evaluate a range of state-of-the-art neural network architectures, including HT-Demucs, MDX23C, and BS-RoFormer, trained using StemGMD, a large-scale dataset of isolated single-instrument drum stems. Besides demonstrating that said architectures out-perform the state-of-the-art method for drum source separation, we discuss their strengths and weaknesses, giving insights into their performance and ultimately offering valuable guidance for researchers and practitioners willing to develop drum demixing models for different applications, among which those related to music making, personalized listening, and online music education stand out.
INTRODUCTION: The combination of sound art and Internet of Things (IoT) technology offers new possibilities for artistic creation. With the rapid development of the Internet of Things (IoT), sound sensors have become a powerful tool for capturing environmental sounds. This study aims to explore the application of suitable sensors in the fusion of painting and music by utilizing IoT technology to create more prosperous and interactive artworks for artists. OBJECTIVES: The study's primary purpose is to explore the potential uses of sound sensors in art creation, especially in integrating painting and music. By deeply analyzing the interaction between sound and visual art, the researchers aim to discover new creative possibilities, thus expanding the boundaries of artistic creation. METHODS: The study utilized a comprehensive methodology that included a literature review, field research, and artistic practice. First, the literature on sound art, IoT, and art fusion was extensively reviewed to provide a theoretical foundation for the study. Second, through field research, the researchers collected practical examples of sound sensors applied in the field of art. Finally, through hands-on art creation in the artist's studio, the researchers verified the potential applications of suitable sensors in the fusion of painting and music. RESULTS: The study found that sound sensors have many applications in painting-music fusion. By embedding suitable sensors into painting tools and music devices, artists can capture the sounds of their surroundings and transform them into visual and auditory artistic expressions. Examples from practice demonstrate how this technological innovation can bring a richer, more sensual experience to artistic creation while engaging the audience in interactive participation. CONCLUSION: The results of this study show that the combination of sound sensors and IoT technology offers new possibilities for artistic creation. By integrating proper elements in painting and music, artists can create more profound and engaging works. This innovation expands the boundaries of art and provides the audience with a more intimate and interactive experience with the artwork. In the future, further research can delve into the application of sound sensors in other artistic fields, thus promoting the integration of art and technology to move forward.
This article reports a theoretical investigation on music curriculum and explores the factors that shape the practice of music education in terms of the official and other music curricula. We drew on relevant literature and explored four key aspects of the design and implementation of music curriculum: policy-making contexts, teaching practices, students’ practices and attitudes, and school contexts. Currently, music education research focuses on student-centredness as an important element in music practice. Embracing students’ various ways of musical engagement as well as their music identities promotes further development of their musical-social skills and enhances their interests. We suggest the development of an updated and comprehensive music curriculum which is student-centred and based on new musical literacies that respond to students’ daily musical needs and address the challenges of today’s social context. Our suggestion is encompassed in five core principles: 1) Getting to know our students, 2) Discussing the teaching-learning goals with our students, 3) Adapting to local conditions, 4) Bottom-up and top-down perspectives: Finding the balance through authentic classroom activities, and 5) Contextualising assessment. The article advocates for the effective use of all types of music curricula so that students become active, responsible and critical thinkers in their personal, social and musical lives.