Folk the Algorithms: (Mis)Applying Artificial Intelligence to Folk Music

Sturm, Bob L.; Ben-Tal, Oded

doi:10.1007/978-3-030-72116-9_16

Cited by 6 publications

(2 citation statements)

References 38 publications

(36 reference statements)

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“…Despite this, the use of multimedia tools and teaching software in the classroom has increased; however, these tools (software) can only be used to aid in teaching, and the existing online courses do not meet the requirements of intelligent teaching. In many visual singing learning systems, there is a paucity of study into the design and implementation of intelligentguided approaches, which is owing to a lack of research into the design and implementation of intelligent-guided approaches [14]. It is necessary to rely on manual assessments to fully automate and intelligently assess learners' skill levels.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence Technology in the Teaching of Complex Situations of Folk Music under the Vision of New Media Art

Ismail

2022

Wireless Communications and Mobile Computing

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Enhancement in information technology has made the online teaching-learning process easier. However, this process is still a challenging task for teaching courses of theoretical type, music, dance, and arts. For the classes of these types, the traditional system of teaching and learning is well suited, but, in particularly complex situations, accomplishing the task is highly difficult. Hence, new media art technology is introduced to overcome the difficulties. In this research, Chinese folk music is taught online with the aid of new media art with the support of artificial intelligence. The analysis of the proposed work is carried out on the folk music dataset, which considers folk music of ethnic minority groups. A novel Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) art algorithm is implemented to perform the analysis. The performance is compared with the existing gradient descent, Adam, and AdaDelta algorithms. L-BFGS algorithm is essentially a particular recipe for designing and possibly executing an artwork, including algorithms, functions, facial expression, and other input that ultimately decides the structure the folk music and media art would then take. This contribution could be numerical, information processing, or formative. From the obtained results, it can be shown that the proposed system has provided 97% and 98% of accuracy on training and testing data, which is higher when compared to the existing algorithms.

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Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence Technology in the Teaching of Complex Situations of Folk Music under the Vision of New Media Art

Ismail

2022

Wireless Communications and Mobile Computing

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“…The first is that whatever AI-powered system comes about that generates jigs and reels, it is likely not going to contribute anything profoundly meaningful to ITM -at least from the perspective of insider practitioners. It is when someone chooses a machine-generated tune and breathes story, life, and history into it that the "process of authentication" can begin and a contribution may be made (Sturm and Ben-Tal, 2018;. 21 Furthermore, considering the role computational technology plays in our world today, why not involve it somehow in making music that could be considered traditional in the future?…”

Section: Case Study 2: Responsible Engineering With Traditional Musicmentioning

confidence: 99%

Beyond Diverse Datasets: Responsible MIR, Interdisciplinarity, and the Fractured Worlds of Music

Huang

Holzapfel²,

Sturm

et al. 2023

Transactions of the International Society for Music Information Retrieval

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Musical worlds, not unlike our lived realities, are fundamentally fragmented and diverse, a fact often seen as a challenge or even a threat to the validity of research in Music Information Research (MIR). In this article, we propose to treat this characteristic of our musical universe(s) as an opportunity to fundamentally enrich and re-orient MIR. We propose that the time has arrived for MIR to reflect on its ethical and cultural turns (if they have been initiated at all) and take them a step further, with the goal of profoundly diversifying the discipline beyond the diversification of datasets. Such diversification, we argue, is likely to remain superficial if it is not accompanied by a simultaneous auto-critique of the discipline's raison d'être. Indeed, this move to diversify touches on the philosophical underpinnings of what MIR is and should become as a field of research: What is music (ontology)? What are the nature and limits of knowledge concerning music (epistemology)? How do we obtain such knowledge (methodology)? And what about music and our own research endeavor do we consider "good" and "valuable" (axiology)? This path involves sincere inter-and intra-disciplinary struggles that underlie MIR, and we point to "agonistic interdisciplinarity" -that we have practiced ourselves via the writing of this article -as a future worth reaching for. The two featured case studies, about possible philosophical re-orientations in approaching ethics of music AI and about responsible engineering when AI meets traditional music, indicate a glimpse of what is possible.

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Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

et al. 2023

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The discipline of Deep Learning has been recognized for its strong computational tools, which have been extensively used in data and signal processing, with innumerable promising results. Among the many commercial applications of Deep Learning, Music Signal Processing has received an increasing amount of attention over the last decade. This work reviews the most recent developments in Deep Learning in Music signal processing. Two main applications that are discussed are Music Information Retrieval, which spans a plethora of applications, and Music Generation, which can fit a range of musical styles. After a review of both topics, several emerging directions are identified for future research.

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Folk the Algorithms: (Mis)Applying Artificial Intelligence to Folk Music

Cited by 6 publications

References 38 publications

Application of Artificial Intelligence Technology in the Teaching of Complex Situations of Folk Music under the Vision of New Media Art

Application of Artificial Intelligence Technology in the Teaching of Complex Situations of Folk Music under the Vision of New Media Art

Beyond Diverse Datasets: Responsible MIR, Interdisciplinarity, and the Fractured Worlds of Music

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

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