Hasil untuk "Musical instruction and study"

Qiong Ge, Jie Lin, Huiling Zhou et al.

<b>Background</b>: This study addresses a common challenge in music education: students’ limited emotional engagement during music listening. <b>Objectives</b>: This study compared two teaching methods—externally guided augmented reality (AR) integration and internally generated simulation—in terms of their neural and behavioral differences in guiding students’ visual mental imagery and influencing their musical affect processing. <b>Methods</b>: Using Chinese Pipa music appreciation as our experimental paradigm, we employed fNIRS hyperscanning to record inter-brain synchronization (IBS) during teacher–student interactions across three instructional conditions (AR group, <i>n</i> = 27; visual imagery group, <i>n</i> = 27; no-instruction group, <i>n</i> = 27), while simultaneously assessing students’ performance in music–emotion processing tasks (emotion recognition and experience). <b>Results</b>: At the behavioral level, both instructional methods significantly enhanced students’ ability to differentiate emotional valence in music compared to the control condition. Crucially, the AR approach demonstrated a unique advantage in augmenting emotional arousal. Neurally, both teaching methods significantly enhanced IBS in brain regions associated with emotion evaluation (lOFC) and imaginative reasoning (bilateral dlPFC). Beyond these shared neural correlates, AR instruction specifically engaged additional brain networks supporting social cognition (lFPC) and multisensory integration (rANG). Furthermore, we identified a significant positive correlation between lFPC-IBS and improved emotional arousal exclusively in the AR group. <b>Conclusions</b>: The visual imagery group primarily enhances emotional music processing through neural alignment in core emotional brain regions, while augmented reality instruction creates unique advantages by additionally activating brain networks associated with social cognition and cross-modal integration. This research provides neuroscientific evidence for the dissociable mechanisms through which different teaching approaches enhance music–emotion learning, offering important implications for developing evidence-based educational technologies.

Eliran Shem-Tov, Ella Rabinovich

Music serves as a powerful reflection of individual identity, often aligning with deeper psychological traits. Prior research has established correlations between musical preferences and personality, while separate studies have demonstrated that personality is detectable through linguistic analysis. Our study bridges these two research domains by investigating whether individuals' musical preferences leave traces in their spontaneous language through the lens of the Big Five personality traits (Openness, Conscientiousness, Extroversion, Agreeableness, and Neuroticism). Using a carefully curated dataset of over 500,000 text samples from nearly 5,000 authors with reliably identified musical preferences, we build advanced models to assess personality characteristics. Our results reveal significant personality differences across fans of five musical genres. We release resources for future research at the intersection of computational linguistics, music psychology and personality analysis.

Mutsumi Kobayashi, Hiroshi Watanabe

We investigate how machine learning models acquire the ability to compose music and how musical information is internally represented within such models. We develop a composition algorithm based on a restricted Boltzmann machine (RBM), a simple generative model capable of producing musical pieces of arbitrary length. We convert musical scores into piano-roll image representations and train the RBM in an unsupervised manner. We confirm that the trained RBM can generate new musical pieces; however, by analyzing the model's responses and internal structure, we find that the learned information is not stored in a form directly interpretable by humans. This study contributes to a better understanding of how machine learning models capable of music composition may internally represent musical structure and highlights issues related to the interpretability of generative models in creative tasks.

Taketo Akama, Zhuohao Zhang, Tsukasa Nagashima et al.

Art has long played a profound role in shaping human emotion, cognition, and behavior. While visual arts such as painting and architecture have been studied through eye tracking, revealing distinct gaze patterns between experts and novices, analogous methods for auditory art forms remain underdeveloped. Music, despite being a pervasive component of modern life and culture, still lacks objective tools to quantify listeners' attention and perceptual focus during natural listening experiences. To our knowledge, this is the first attempt to decode selective attention to musical elements using naturalistic, studio-produced songs and a lightweight consumer-grade EEG device with only four electrodes. By analyzing neural responses during real world like music listening, we test whether decoding is feasible under conditions that minimize participant burden and preserve the authenticity of the musical experience. Our contributions are fourfold: (i) decoding music attention in real studio-produced songs, (ii) demonstrating feasibility with a four-channel consumer EEG, (iii) providing insights for music attention decoding, and (iv) demonstrating improved model ability over prior work. Our findings suggest that musical attention can be decoded not only for novel songs but also across new subjects, showing performance improvements compared to existing approaches under our tested conditions. These findings show that consumer-grade devices can reliably capture signals, and that neural decoding in music could be feasible in real-world settings. This paves the way for applications in education, personalized music technologies, and therapeutic interventions.

Liu Qian

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.

John Hewitt, Nelson F. Liu, Percy Liang et al.

Instruction tuning commonly means finetuning a language model on instruction-response pairs. We discover two forms of adaptation (tuning) that are deficient compared to instruction tuning, yet still yield instruction following; we call this implicit instruction tuning. We first find that instruction-response pairs are not necessary: training solely on responses, without any corresponding instructions, yields instruction following. This suggests pretrained models have an instruction-response mapping which is revealed by teaching the model the desired distribution of responses. However, we then find it's not necessary to teach the desired distribution of responses: instruction-response training on narrow-domain data like poetry still leads to broad instruction-following behavior like recipe generation. In particular, when instructions are very different from those in the narrow finetuning domain, models' responses do not adhere to the style of the finetuning domain. To begin to explain implicit instruction tuning, we hypothesize that very simple changes to a language model's distribution yield instruction following. We support this by hand-writing a rule-based language model which yields instruction following in a product-of-experts with a pretrained model. The rules are to slowly increase the probability of ending the sequence, penalize repetition, and uniformly change 15 words' probabilities. In summary, adaptations made without being designed to yield instruction following can do so implicitly.

Ziya Zhou, Yuhang Wu, Zhiyue Wu et al.

Symbolic Music, akin to language, can be encoded in discrete symbols. Recent research has extended the application of large language models (LLMs) such as GPT-4 and Llama2 to the symbolic music domain including understanding and generation. Yet scant research explores the details of how these LLMs perform on advanced music understanding and conditioned generation, especially from the multi-step reasoning perspective, which is a critical aspect in the conditioned, editable, and interactive human-computer co-creation process. This study conducts a thorough investigation of LLMs' capability and limitations in symbolic music processing. We identify that current LLMs exhibit poor performance in song-level multi-step music reasoning, and typically fail to leverage learned music knowledge when addressing complex musical tasks. An analysis of LLMs' responses highlights distinctly their pros and cons. Our findings suggest achieving advanced musical capability is not intrinsically obtained by LLMs, and future research should focus more on bridging the gap between music knowledge and reasoning, to improve the co-creation experience for musicians.

SeungHeon Doh, Minhee Lee, Dasaem Jeong et al.

Text-to-Music Retrieval, finding music based on a given natural language query, plays a pivotal role in content discovery within extensive music databases. To address this challenge, prior research has predominantly focused on a joint embedding of music audio and text, utilizing it to retrieve music tracks that exactly match descriptive queries related to musical attributes (i.e. genre, instrument) and contextual elements (i.e. mood, theme). However, users also articulate a need to explore music that shares similarities with their favorite tracks or artists, such as \textit{I need a similar track to Superstition by Stevie Wonder}. To address these concerns, this paper proposes an improved Text-to-Music Retrieval model, denoted as TTMR++, which utilizes rich text descriptions generated with a finetuned large language model and metadata. To accomplish this, we obtained various types of seed text from several existing music tag and caption datasets and a knowledge graph dataset of artists and tracks. The experimental results show the effectiveness of TTMR++ in comparison to state-of-the-art music-text joint embedding models through a comprehensive evaluation involving various musical text queries.

Amit Sharma, A. Yadav, Honey Singh et al.

Academic efforts to enhance erhu instruction reflect a wider educational reform integrating advanced technologies and innovative frameworks to enrich traditional Chinese musical instrument learning. Deep learning’s role in erhu education promises to revolutionize teaching methods through personalized learning paths and adaptive strategies, enhancing student experiences. The study focuses on the construction of a personalized erhu teaching system based on deep learning. It mentions the utilization of deep learning for offering customized learning paths and adaptive teaching frameworks, aiming to improve the quality of music education and provide students with personalized learning experience. The optimal configuration led to a model with 203,338 trainable parameters, achieving an impressive 93.87% accuracy. This high accuracy, demonstrated through detailed training/validation loss and accuracy plots over 150 epochs to prevent over fitting, and a confusion matrix with minimal classifications, underscores the potential of deep learning in enhancing music genre classification methodologies.

Pramantha Tagore

In the final decades of the nineteenth century, music significantly occupied the cultural and social life of the Bengali people. As the epicenter of British political and economic influence in the subcontinent, Calcutta witnessed the emergence of schools offering instruction in Indian and Western art music. The flourishing city housed private and public printing presses, which ensured the circulation and distribution of large numbers of songbooks, manuals, and theoretical treatises on music. The city was also home to a diverse assortment of hereditary music practitioners and occupational specialists illustrative of a variety of musical traditions spread across Bengal and North India. Around the 1870s, Bengali musicians, patrons, and connoisseurs began to take up music as an intellectual activity, examine its history as a source for social and political substance, and view musical instruments as material objects for disciplinary study. This emerging interest in musicology, broadly conceived, coincided with the proclamation of Victoria as queen and empress of India, considerably transforming Bengal's political fabric and cultural worldview. The pioneering musicologist Sourindro Mohun Tagore (1840–1914) was among the many authors who published works celebrating Queen Victoria's ascension as empress of India. In this article, I examine Tagore's songbooks dedicated to the queen, reading them as cultural artifacts representing a richly nuanced historical and musical legacy: a textual and aural archive demonstrating how Bengali musicians used sound to mediate the effects of colonization.

Shi-ying Wang, Zuraimy Mohamed Noordin

This study investigates the impact of implementing the Orff music teaching method on student performance and engagement in basic music courses for Primary Education Majors at Jiangxi University of Technology, China. Employing a mixed-methods approach, the study examines the effectiveness of the Orff method compared to traditional music theory and solfeggio instruction, as well as the relationship between student participation and engagement levels in music classes and their performance. Quantitative analysis of survey data from 379 participants reveals a significant positive correlation between the Orff method and student performance, with students demonstrating higher levels of engagement and understanding of fundamental musical concepts. Qualitative analysis of open-ended responses highlights the interactive and collaborative nature of Orff-based activities, fostering a sense of community and creativity within the classroom. The findings underscore the potential of the Orff method to revolutionize music education practices, emphasizing active learning, creativity, and cultural sensitivity.

Jaeran Lee, Yunhee Seung

Objectives This study aims to analyze the changes in the content of music education from the first curriculum of special education to the latest curriculum and provide the characteristics and implications of music textbooks by each period. Methods The analysis of music curriculums was focused on the composition of curriculum documents and the changes in content; 11 music textbooks were analyzed from the perspectives of “music area and activity,” and “musical concept and element.” For music area and activity, the ratios of music area and activity type were ana-lyzed, and for musical concept and element, learning content and the grade hierarchy of learning content were analyzed. Results The content and content composition of music curriculums have been organized the same as those of the general curriculums since the 2011 curriculum. Music element was suggested with a focus on “sound” in the 2008 and 2011 curriculums, but it was mostly replaced with musical terms in the 2015 and 2022 curriculums. First, in the textbook analysis for the ratio of music areas, the area of “appreciation” was the highest in the 2008 and 2011 music textbooks and the area of “singing” was the highest in 2015 music textbook. When it comes to the ratio of activity types, the activity related to “sound” was the highest across the areas of singing, instrument, and appreciation in the 2008 music textbook. In the 2011 and 2015 music textbooks, activity related to “musical concept and element” was the highest. In the area of creation, two activities, “making sounds” and “changing lyrics,” were most frequently observed. In the area of daily life, the activity of “play” was the highest across all the textbooks. Second, the results of analyzing the musical concept and element showed that the level of the 2008 music textbook was similar to the lower grades of other textbooks, and the 2011 and 2015 music textbooks had a similar level of learning contents on “rhythm, form, tempo, and dynamic”. On the other hand, in terms of “melody” and “timbre,” association and hierarchy between grades had no distinct differences, and “harmony” was not sug-gested in most of the textbooks. In activities related to the characteristics of sound, instructional errors were found in the association with musical concepts. Conclusions The scope and level of music education content in special schools need to be improved; simulta-neously, research on systematic teaching and learning is required. This study explored the trend in music cur-riculums and the results of analyzing music textbooks. The findings are expected to provide useful basic data for developing the music curriculum of special education and for writing elementary school music textbooks. Further research should be conducted based on this study.

Ilfan Triatmiko

This research is motivated by the uniqueness of the various patterns of Tahar wasps which are different from other Tahar wasps. The purpose of this study is to describe the technique of playing and analyzing the various patterns of the tahar wasp. The method used is a descriptive method with a qualitative research form. The approach used is a musicological approach. Sources of data in this study are Rabudin, Yuhendri and Mulyadi. The data of this research are the results of interviews, observations, documentation and notebooks regarding various patterns of tahar wasps and cameras. Checking the validity of the data in this study by triangulation and extension of observations. Data analysis in this study uses three flow of activities, namely data reduction, data presentation, and drawing conclusions. The results of this study are the technique of playing and various patterns of Tahar wasps in hadrah art in Tumuk Manggis Village, Sambas Regency. Tahar musical instruments in this hadrah art have two sound colors, namely "tak" and "dung". There are three kinds of Tahar wasp patterns in hadrah art in the village of Tumuk Manggis, Sambas Regency, namely the kutong, tarrus, and short rabbut patterns using the 4/4 bar

M. J. Lee, HeeTae Jeong

Objectives This study analyzed the curriculum competencies of the 2022 revised music curriculum and explored the contents of classical music that can foster them. Methods In order to explore the contents of classical music that can cultivate the five curricular competencies of the 2022 revised music curriculum, composers, works, and musical activities of classical music in the 2015 revised middle school music textbooks were explored. Results ‘Emotional Competence’ can be cultivated through ‘Emotional theory’ of the Baroque period, ‘Over- emotional style’, and works of the Romantic period, and ‘Communication Competence’ can be cultivated through three communication structures in music. Also, ‘Community Competence’ can be cultivated through classical music and folk songs of various periods and cultures, and ‘Self-directed Competence’ can be cultivated through ‘Musical play’. Finally, ‘Creative Competence’ can be cultivated through composing techniques of Wagner, Schoenberg, Debussy, and lyrics of songs. Conclusions This study is the first study on the cultivation of five music curriculum competencies in the 2022 revised music curriculum using classical music, which means that the value of classical music has been confirmed in the curriculum.

Kangwon Kim

Objectives The purpose of this study was to explore and discover critical issues of Western music education regarding improvisation, and to analyze and discuss them. Methods As a systematic literature study, strategies such as database searches, backward snowballing, and forward snowballing were used to collect data. English words, improvisation, music, education were used as key words for data searches to identify relevant English literature. In order to discover potential critical issues gathered literature were thoroughly reviewed and re-analyzed by realms. Results Recurring important issues of improvisation in Western music education were divided into four realms. Those were issues relating to defining the term, diverse spectrum of improvisation, the critique of current pedagogies, and music teachers’ avoidance of improvisational activities. Also, the second issue was subdivided into three. Those were improvisation as musical traditions, improvisation as musical activities, and improvisation as pedagogical approaches. Conclusions This study suggested defining improvisation in an educational context, having a sensitivity to a degree of freedom given for students’ musical choices, and taking action to incorporate improvising activities in music teacher education.

Emanuele Cosenza, Andrea Valenti, Davide Bacciu

Graphs can be leveraged to model polyphonic multitrack symbolic music, where notes, chords and entire sections may be linked at different levels of the musical hierarchy by tonal and rhythmic relationships. Nonetheless, there is a lack of works that consider graph representations in the context of deep learning systems for music generation. This paper bridges this gap by introducing a novel graph representation for music and a deep Variational Autoencoder that generates the structure and the content of musical graphs separately, one after the other, with a hierarchical architecture that matches the structural priors of music. By separating the structure and content of musical graphs, it is possible to condition generation by specifying which instruments are played at certain times. This opens the door to a new form of human-computer interaction in the context of music co-creation. After training the model on existing MIDI datasets, the experiments show that the model is able to generate appealing short and long musical sequences and to realistically interpolate between them, producing music that is tonally and rhythmically consistent. Finally, the visualization of the embeddings shows that the model is able to organize its latent space in accordance with known musical concepts.

Po-Nien Kung, Nanyun Peng

Recent works on instruction tuning (IT) have achieved great performance with zero-shot generalizability to unseen tasks. With additional context (e.g., task definition, examples) provided to models for fine-tuning, they achieved much higher performance than untuned models. Despite impressive performance gains, what models learn from IT remains understudied. In this work, we analyze how models utilize instructions during IT by comparing model training with altered vs. original instructions. Specifically, we create simplified task definitions by removing all semantic components and only leaving the output space information, and delusive examples that contain incorrect input-output mapping. Our experiments show that models trained on simplified task definition or delusive examples can achieve comparable performance to the ones trained on the original instructions and examples. Furthermore, we introduce a random baseline to perform zeroshot classification tasks, and find it achieves similar performance (42.6% exact-match) as IT does (43% exact-match) in low resource setting, while both methods outperform naive T5 significantly (30% per exact-match). Our analysis provides evidence that the impressive performance gain of current IT models can come from picking up superficial patterns, such as learning the output format and guessing. Our study highlights the urgent need for more reliable IT methods and evaluation.

Adarsh Kumar, Pedro Sarmento

Subword tokenization has been widely successful in text-based natural language processing (NLP) tasks with Transformer-based models. As Transformer models become increasingly popular in symbolic music-related studies, it is imperative to investigate the efficacy of subword tokenization in the symbolic music domain. In this paper, we explore subword tokenization techniques, such as byte-pair encoding (BPE), in symbolic music generation and its impact on the overall structure of generated songs. Our experiments are based on three types of MIDI datasets: single track-melody only, multi-track with a single instrument, and multi-track and multi-instrument. We apply subword tokenization on post-musical tokenization schemes and find that it enables the generation of longer songs at the same time and improves the overall structure of the generated music in terms of objective metrics like structure indicator (SI), Pitch Class Entropy, etc. We also compare two subword tokenization methods, BPE and Unigram, and observe that both methods lead to consistent improvements. Our study suggests that subword tokenization is a promising technique for symbolic music generation and may have broader implications for music composition, particularly in cases involving complex data such as multi-track songs.

Arvind Shankar Kumar

This is an introductory review of Musical Excellence of Mridangam by Dr. Umayalpuram K Sivaraman, Dr. T Ramasami and Dr. Naresh, which is a scientific treatise exploring the unique tonal properties of the ancient Indian classical percussive instrument -- the Mridangam. This review aims to bridge the gap between the primary intended audience of Musical Excellence of Mridangam - listeners, artistes and makers -- and the scientific rigour with which the original treatise is written, by first introducing the concepts of musical analysis and then presenting and explaining the discoveries made within this context. The first three chapters of this review introduce the basic scientific concepts used in Musical Excellence of Mridangam and provides background to previous scientific research into this instrument, starting from the seminal work of Dr. CV Raman. This also includes brief discussions of the corresponding chapters in Musical Excellence of Mridangam. The next chapters all serve the purpose of explaining the main scientific results presented in Musical Excellence of Mridangam in each of the corresponding chapters in the treatise, and finally summarizing the relevance of the work.

Florian Worschech, E. Altenmüller, Kristin Jünemann et al.

Morphological differences in the auditory brain of musicians compared to nonmusicians are often associated with life‐long musical activity. Cross‐sectional studies, however, do not allow for any causal inferences, and most experimental studies testing music‐driven adaptations investigated children. Although the importance of the age at which musical training begins is widely recognized to impact neuroplasticity, there have been few longitudinal studies examining music‐related changes in the brains of older adults. Using magnetic resonance imaging, we measured cortical thickness (CT) of 12 auditory‐related regions of interest before and after 6 months of musical instruction in 134 healthy, right‐handed, normal‐hearing, musically‐naive older adults (64–76 years old). Prior to the study, all participants were randomly assigned to either piano training or to a musical culture/music listening group. In five regions—left Heschl's gyrus, left planum polare, bilateral superior temporal sulcus, and right Heschl's sulcus—we found an increase in CT in the piano training group compared with the musical culture group. Furthermore, CT of the right Heschl's gyrus could be identified as a morphological substrate supporting speech in noise perception. The results support the conclusion that playing an instrument is an effective stimulator for cortical plasticity, even in older adults.