Andrea Monferrer-Montañés, Pablo Marín Liébana, Gemma Ruiz-Varela
et al.
La música escénica, como una extensión natural de las pedagogías activas y creativas, ha emergido como una tendencia en la educación musical, alineándose con enfoques que promueven el aprendizaje experiencial y el desarrollo integral del alumnado. Diversas investigaciones han señalado sus múltiples beneficios educativos, destacando su capacidad para fomentar la creatividad, aumentar la motivación y generar experiencias emocionales positivas. Asimismo, su práctica favorece la participación activa del alumnado, el desarrollo de la conciencia social, el compromiso personal y el reconocimiento de la diversidad cultural, aspectos clave en la formación de ciudadanos críticos y reflexivos. Además, el currículo oficial actual prescribe su incorporación en la educación secundaria, reconociendo de esta forma su valor pedagógico. No obstante, la investigación sobre su representación en los materiales educativos es limitada, especialmente en los libros de texto, que continúan desempeñando un papel fundamental en el proceso de enseñanza-aprendizaje. Este estudio tiene como objetivo analizar la presencia y el tratamiento didáctico de la música escénica en los libros de texto más utilizados en los institutos públicos de educación secundaria de Castellón (Comunidad Valenciana). Para ello, se ha aplicado una metodología de análisis de contenido a una muestra de 748 actividades en ocho manuales escolares. Los resultados muestran que la música escénica representa aproximadamente el 25% del contenido musical, con un predominio de la banda sonora y una presencia reducida del teatro musical, la zarzuela y la música incidental. Las propuestas didácticas se centran principalmente en la audición receptiva y en contenidos teóricos, relegando a un segundo plano la creación, el movimiento y los proyectos interdisciplinares. El estudio concluye que, para aprovechar plenamente su potencial educativo, la música escénica debería abordarse mediante metodologías más dinámicas, experienciales y creativas.
This paper, grounded in a bibliographic review, aims to highlight the methodological and philosophical dialogues between the Triangular Approach, systematized by Brazilian art educator Ana Mae Barbosa, and Orff-Schulwerk, understood here as the central pedagogical and musical framework of reflection. Drawing on key concepts, we argue that the Triangular Approach’s vertices – artistic making, historical contextualization, and the critical reading of the world through art – resonate with the ideas of Carl Orff and Gunild Keetman. These ideas gain further depth when situated within an active, critical, and culturally grounded perspective of music education. In this sense, we contend that both approaches find fertile ground in aesthetic- musical experiences that extend beyond the act of teaching and learning, opening space for deeper inquiry into what is taught, why it is taught, and how such knowledge connects with learners’ sensory, critical, and cultural dimensions.
Zipf's law, originally discovered in natural language and later generalized to the Zipf-Mandelbrot law, describes a power-law relationship between the frequency of a Zipfian element and its rank. Due to the semantic characteristics of this law, it has also been observed in musical data. However, most such studies have focused on Western music, and its applicability to non-Western music remains not well investigated. We analyzed 43 Korean court music pieces called Jeong-ak, spanning several centuries and written in the traditional Korean musical notation Jeongganbo. These pieces were transcribed into Western staff notation, and musical data such as pitch and duration were extracted. Using pitch, duration, and their paired combinations as Zipfian units, we found that Korean music also fits the Zipf-Mandelbrot law to a high degree, particularly for the paired pitch-duration unit. Korean music has evolved collectively over long periods, smoothing idiosyncratic variations and producing forms that are widely understandable among people. This collective evolution appears to have played a significant role in shaping the characteristics that lead to the satisfaction of Zipf-Mandelbrot law. Our findings provide additional evidence that Zipf-Mandelbrot scaling in musical data is universal across cultures. We further show that the joint distribution of two independent Zipfian data sets follows the Zipf-Mandelbrot law; in this sense, our result does not merely extend Zipf's law but deepens our understanding of how scaling laws behave under composition and interaction, offering a more unified perspective on rank-based statistical regularities.
Daniel Chenyu Lin, Michael Freeman, John Thickstun
Music language models (Music LMs), like vision language models, leverage multimodal representations to answer natural language queries about musical audio recordings. Although Music LMs are reportedly improving, we find that current evaluations fail to capture whether their answers are correct. Specifically, for all Music LMs that we examine, widely-used evaluation metrics such as BLEU, METEOR, and BERTScore fail to measure anything beyond linguistic fluency of the model's responses. To measure the true performance of Music LMs, we propose (1) a better general-purpose evaluation metric for Music LMs adapted to the music domain and (2) a factual evaluation framework to quantify the correctness of a Music LM's responses. Our framework is agnostic to the modality of the question-answering model and could be generalized to quantify performance in other open-ended question-answering domains. We use open datasets in our experiments and will release all code on publication.
AI systems for music generation are increasingly common and easy to use, granting people without any musical background the ability to create music. Because of this, generative-AI has been marketed and celebrated as a means of democratizing music making. However, inclusivity often functions as marketable rhetoric rather than a genuine guiding principle in these industry settings. In this paper, we look at four generative-AI music making systems available to the public as of mid-2025 (AIVA, Stable Audio, Suno, and Udio) and track how they are rhetoricized by their developers, and received by users. Our aim is to investigate ideologies that are driving the early-stage development and adoption of generative-AI in music making, with a particular focus on democratization. A combination of autoethnography and digital ethnography is used to examine patterns and incongruities in rhetoric when positioned against product functionality. The results are then collated to develop a nuanced, contextual discussion. The shared ideology we map between producers and consumers is individualist, globalist, techno-liberal, and ethically evasive. It is a 'total ideology' which obfuscates individual responsibility, and through which the nature of music and musical practice is transfigured to suit generative outcomes.
Musical expressivity and coherence are indispensable in music composition and performance, while often neglected in modern AI generative models. In this work, we introduce a listening-based data-processing technique that captures the expressivity in musical performance. This technique derived from Weber's law reflects the human perceptual truth of listening and preserves musical subtlety and expressivity in the training input. To facilitate musical coherence, we model the output interdependencies among multiple arguments in the music data such as pitch, duration, velocity, etc. in the neural networks based on the probabilistic chain rule. In practice, we decompose the multi-output sequential model into single-output submodels and condition previously sampled outputs on the subsequent submodels to induce conditional distributions. Finally, to select eligible sequences from all generations, a tentative measure based on the output entropy was proposed. The entropy sequence is set as a criterion to select predictable and stable generations, which is further studied under the context of informational aesthetic measures to quantify musical pleasure and information gain along the music tendency.
Automatic Music Transcription (AMT) -- the task of converting music audio into note representations -- has seen rapid progress, driven largely by deep learning systems. Due to the limited availability of richly annotated music datasets, much of the progress in AMT has been concentrated on classical piano music, and even a few very specific datasets. Whether these systems can generalize effectively to other musical contexts remains an open question. Complementing recent studies on distribution shifts in sound (e.g., recording conditions), in this work we investigate the musical dimension -- specifically, variations in genre, dynamics, and polyphony levels. To this end, we introduce the MDS corpus, comprising three distinct subsets -- (1) Genre, (2) Random, and (3) MAEtest -- to emulate different axes of distribution shift. We evaluate the performance of several state-of-the-art AMT systems on the MDS corpus using both traditional information-retrieval and musically-informed performance metrics. Our extensive evaluation isolates and exposes varying degrees of performance degradation under specific distribution shifts. In particular, we measure a note-level F1 performance drop of 20 percentage points due to sound, and 14 due to genre. Generally, we find that dynamics estimation proves more vulnerable to musical variation than onset prediction. Musically informed evaluation metrics, particularly those capturing harmonic structure, help identify potential contributing factors. Furthermore, experiments with randomly generated, non-musical sequences reveal clear limitations in system performance under extreme musical distribution shifts. Altogether, these findings offer new evidence of the persistent impact of the Corpus Bias problem in deep AMT systems.
Learning guzheng has gradually evolved into a cultural phenomenon that not only sweeps across the country but also exerts influence overseas. However, traditional guzheng teaching is characterized by monotonous methodologies, lack of engaging elements, and insufficient cultivation of musical expressiveness as well as artistic sensibility. Therefore, grounded in constructivist theory and using "Fighting the Typhoon" as a case study, this paper develops a teaching design aimed at enhancing the quality of guzheng instruction, fostering students' autonomous learning capacity, and nurturing their musical innovation skills.
Objectives The purpose of this study is to identify the structural relationship between early childhood teaches’ musical interest, music self-concept, knowledge for early childhood music activities, attitude toward early childhood music education, and the practice of early childhood music education. Through this, this study seeks the ways to support teacher education, and intended to provide a practical and theorietical basis for increasing the effictiveness of music education in early childhood education. Methods The participants of this study were 278 kindergarten teachers in Seoul, Gyeonggi, and the data was analyzed using SPSS 18.0 and AMOS 18.0. To ensure the validity of the research model, convergent validity and discriminant validity were verified. In structural equation model, after checking the research model fit index, Afterwards, path analysis was performed and the direct effect, indirect effect, and total effect were verified. And through bootstrapping, the statistical significance of mediation effect was confirmed. Results As a result, it was found that musical self-concept, knowledge for early childhood music activities, attitude toward early childhood music education had a mediating effect in the relationship between the musical interest and the practice of early childhood music education. Conclusions The findings highlight the importance of early childhood teachers not only having an interest in music but also cultivating a positive perception of their musical abilities, acquiring systematic knowledge of music activities, and fostering a favorable attitude toward music education. To enhance the quality of early childhood music education practices, it is essential to promote teachers' interest in music while simultaneously strengthening teacher education programs and implementing educational initiatives that expand their knowledge of early childhood music activities and support the development of positive attitudes toward music education.
Objectives This study aims to analyze the impact of experiential teaching methods based on the Zone of Proximal Development (ZPD) theory on the learning engagement of 4- to 6-year-old beginner piano learners. It also seeks to identify the mechanisms through which the experiential elements in Faber’s (First Adventures) textbook stimulate musical interest and participation in beginner learners. Methods A mixed-methods approach was adopted, targeting 100 instructors teaching beginner piano learners in Seoul. Quantitative surveys were analyzed using SPSS software to conduct descriptive statistics and path analysis, verifying the direct and indirect relationships between classroom behavior, learning interest, and teaching effectiveness. Open-ended survey responses were analyzed through thematic analysis using Nvivo software, extracting key themes to explore instructors’ practical feedback on Faber’s My First (First Adventures) textbook and experiential teaching methods. This provided insights into how these methods stimulate learning interest and enhance teaching effectiveness. Results The results revealed that experiential teaching methods significantly and directly influence beginner learners’ interest and classroom behavior (β = 0.380, p < 0.001). The dimension of learning interest scored the highest (mean = 4.45, standard deviation = 0.54), highlighting its critical role. Furthermore, learning interest demonstrated a significant partial mediating effect between classroom behavior and teaching effectiveness (indirect effect β = 0.260, total effect β = 0.640, p < 0.001). These findings suggest that experiential teaching methods enhance learning interest and optimize classroom behavior, thereby indirectly improving teaching quality and learning outcomes. Conclusions Experiential teaching methods effectively stimulate beginners’ interest and optimize classroom behavior through gamified designs and interactive activities in the My First textbook, providing an effective pathway for addressing the educational needs of 4- to 6-year-old beginner piano learners. However, a balance must be struck in the design and implementation between generating interest and providing sufficient practice opportunities. The findings offer empirical evidence for instructional material design and teaching practices in music education.
Objectives This study aims to analyze whether music pieces reflecting a balanced variety of music types and elements have been selected as repertoire in music textbooks and to propose directions for developing high-quality music textbooks. Methods A total of 190 pieces from the music textbooks of the Basic Curriculum for Special Schools, spanning from the 7th Curriculum to the 2015 Revised Basic Curriculum for Special Education, were analyzed. To analyze the components, seven musical elements were divided into 15 sub-elements for detailed examination. Results The analysis of the repertoire revealed limitations in music type and imbalances or biases in musical concepts, as follows: First, in the timbre analysis, vocal music was predominant; in the rhythm analysis, most pieces were limited to 4/4 time; and in the melody analysis, major-key pieces were overwhelmingly represented. Second, in the harmony analysis, the majority of pieces were biased toward unison singing, lacking the concept of harmony. Additionally, pieces tended to focus on specific vocal forms such as binary and ternary forms. Third, in the analysis of tempo and dynamics, many pieces either lacked markings or used incorrect notations, with a significant proportion skewed toward moderate tempo. Conclusions To improve the internal structure of textbooks, it is suggested that the selection of repertoire should reflect a diverse and balanced range of music types and concepts. Concrete guidelines for this process should be established. Moreover, teaching adaptations should be applied in textbooks to enhance accessibility to musical concepts. To improve the external structure, it is recommended to strengthen the review process after textbook development and establish a continuous research and review system for textbooks.
This research investigated the habitual integration of musical instruments in vocal stage performances, focusing on how self-accompaniments influences technical performance, confidence and artistic execution. The study examined the complex reasons for using instruments such as harmonium, tanpura and swarmandal in Hindustani music. It utilized qualitative data gathered from professional classical vocalists and music educators within the Sri Lankan context. Key findings reveal that self-accompaniment serves not only as a pedagogical and expressive tool, also as a source of psychological comfort and personal identity for performers. Furthermore, the impact of musical idols and stage dynamics significantly shapes the development of these performance habits. This paper contributes to ongoing discussions in music pedagogy and performance psychology, emphasizing the evolving and multidimensional role of instruments in vocal artistry.
This research examines the transformative role of prompt engineering in optimizing ChatGPT's utility in a digital-system-design class at Embry-Riddle Aeronautical University. By exploring various prompt engineering strategies, the authors investigate how to tailor prompts to significantly enhance ChatGPT's ability to assist students in complex coding and debugging tasks. The study highlights the pivotal role of prompt engineering in elevating ChatGPT from a basic informational tool to an integral component of the educational process. The chosen course centers on using FPGAs to generate and apply state machines. One course activity involves generating musical tones and sequentially playing them to produce the specified songs. Another activity challenges students to construct the foundational elements of a simple reduced instruction set processor, which includes defining a custom instruction set and conforming to a 16-bit design specification. This process entails developing an arithmetic logic unit, creating a general register array, and formulating the processor's specific instruction set. Therefore, this research is a pilot study with a small sample of students using ChatGPT to examine what prompts work best when used in a technical programming class.
This article presents a descriptive analysis of eight music teacher education programs in Puerto Rico. Within the international landscape, music education in Puerto Rico faces obstacles similar to other countries in the form of government cutbacks and limited offerings in school settings. This study presents an analysis and evaluation of the curricular characteristics of music education bachelor’s degrees and a depiction of preservice music teacher education in the country. This was achieved through the use of the Project ALFA II-0448-A analysis matrix, an instrument that compiles four forms of data: (a) general information; (b) history of the program, (c) structure and duration; and (d) curricular principles. Information was obtained from the official web pages of each institution and/or department. Comparisons with programs in other countries reveal a propensity to adopt foreign music education models, with our analysis also outlining the implications of adopting external approaches. Musical instruction in Puerto Rican music teaching degrees is exclusively on Western art music; none of the programs offer musical instruction through popular music or the country’s traditional music or music-pedagogical lessons conducive to teaching such art forms. The lack of education in popular and Indigenous music renders it challenging for music educators to offer school-level students culturally responsive music lessons. The case of Puerto Rico thus demonstrates the perpetuation of European cultural hegemony in higher music education spaces, driven by adherence to foreign standards and approaches to music pedagogy that emanate from the global north.
Educational technology has altered conceptions of effective learning. The combination of virtual and physical instruction is constantly developing and growing, making blended learning a growing trend. While blended learning has been extensively reviewed in various fields, there is a lack of research on its application in music education. This study aims to understand the current development of blended learning in the field of music education; this paper will collect literature on blended learning from the past ten years. Following the PRISMA guidelines, we have systematically analysed the blended learning strategies and outcomes in music education. This study examined Web of Science, Scopus, and ERIC for pertinent strings. Thirteen publications passed the strict screening procedure and were included in the analysis of blended learning in music education. This study found that the flipped classroom blended learning model has emerged as the prevailing method in music education, enhancing learners’ musical perception, performance, and overall experience. The findings reveal a need for more research on blended learning in music education.
This qualitative study explored the need to modernize music programmes in higher education with popular music to prepare graduates for twenty-first-century employment. Researchers agreed that popular music studies will allow students to gain valuable knowledge and use their degree in complex systems in various music professions. This study utilized Tyler’s Basic Principles of Curriculum and Instruction Rationale Model to discover what students need to be successful in a music career, with consideration of modern-day influences and popular music. Ten interviews were conducted with professionals in various music careers to raise awareness of music employment and the need for change in higher education to support life post-graduation. The findings showed a consensus that popular music should be added to the music curriculum. Future research recommendations include surveying students regarding their musical interests and influences, implementing popular music within the traditional curriculum and application-based learning.
In the backdrop of evolving technological landscapes, this paper delves into the utilization of computer technology, specifically Artificial Neural Networks (ANN), in enhancing piano improvisation instruction. Recognizing the growing demand for innovative teaching methods, our study aims to evaluate the practical effectiveness of ANN-based teaching evaluation in real-world piano improvisation settings. Through rigorous testing, we found that ANN demonstrates remarkable precision and consistency in assessing piano improvisation skills. In comparison to the traditional decision tree algorithm, ANN excels in managing complex nonlinear relationships, providing more accurate and reliable scoring results. This integration of technology not only elevates students’ performance but also fosters their musical creativity and perception. Our findings suggest potential improvements in refining instructional methodologies and expanding the use of computer technology in piano teaching. This underscores the importance of personalized teaching, blended learning models, and technical proficiency training for educators. Overall, our research methodologies and findings significantly contribute to advancing the modernization and technological progress of music education, equipping piano instructors with cutting-edge teaching tools and strategies.
Intent classification is a text understanding task that identifies user needs from input text queries. While intent classification has been extensively studied in various domains, it has not received much attention in the music domain. In this paper, we investigate intent classification models for music discovery conversation, focusing on pre-trained language models. Rather than only predicting functional needs: intent classification, we also include a task for classifying musical needs: musical attribute classification. Additionally, we propose a method of concatenating previous chat history with just single-turn user queries in the input text, allowing the model to understand the overall conversation context better. Our proposed model significantly improves the F1 score for both user intent and musical attribute classification, and surpasses the zero-shot and few-shot performance of the pretrained Llama 3 model.
The rapid development of musical AI technologies has expanded the creative potential of various musical activities, ranging from music style transformation to music generation. However, little research has investigated how musical AIs can support music therapists, who urgently need new technology support. This study used a mixed method, including semi-structured interviews and a participatory design approach. By collaborating with music therapists, we explored design opportunities for musical AIs in music therapy. We presented the co-design outcomes involving the integration of musical AIs into a music therapy process, which was developed from a theoretical framework rooted in emotion-focused therapy. After that, we concluded the benefits and concerns surrounding music AIs from the perspective of music therapists. Based on our findings, we discussed the opportunities and design implications for applying musical AIs to music therapy. Our work offers valuable insights for developing human-AI collaborative music systems in therapy involving complex procedures and specific requirements.
Comprehending natural language instructions is a charming property for both 2D and 3D layout synthesis systems. Existing methods implicitly model object joint distributions and express object relations, hindering generation's controllability. We introduce InstructLayout, a novel generative framework that integrates a semantic graph prior and a layout decoder to improve controllability and fidelity for 2D and 3D layout synthesis. The proposed semantic graph prior learns layout appearances and object distributions simultaneously, demonstrating versatility across various downstream tasks in a zero-shot manner. To facilitate the benchmarking for text-driven 2D and 3D scene synthesis, we respectively curate two high-quality datasets of layout-instruction pairs from public Internet resources with large language and multimodal models. Extensive experimental results reveal that the proposed method outperforms existing state-of-the-art approaches by a large margin in both 2D and 3D layout synthesis tasks. Thorough ablation studies confirm the efficacy of crucial design components.