Hasil untuk "Music and books on Music"

Joonwon Seo, Mariana Montiel

Classical Recurrent Neural Networks (RNNs) summarize musical context into a deterministic hidden state vector, imposing an information bottleneck that fails to capture the inherent ambiguity in music. We propose the Density Matrix RNN (DM-RNN), a novel theoretical architecture utilizing the Density Matrix. This allows the model to maintain a statistical ensemble of musical interpretations (a mixed state), capturing both classical probabilities and quantum coherences. We rigorously define the temporal dynamics using Quantum Channels (CPTP maps). Crucially, we detail a parameterization strategy based on the Choi-Jamiolkowski isomorphism, ensuring the learned dynamics remain physically valid (CPTP) by construction. We introduce an analytical framework using Von Neumann Entropy to quantify musical uncertainty and Quantum Mutual Information (QMI) to measure entanglement between voices. The DM-RNN provides a mathematically rigorous framework for modeling complex, ambiguous musical structures.

Daeun Hwang, Xuyuan Cai, Edward F. Melcer et al.

Video game music (VGM) is often studied under the same lens as film music, which largely focuses on its theoretical functionality with relation to the identified genres of the media. However, till date, we are unaware of any systematic approach that analyzes the quantifiable musical features in VGM across several identified game genres. Therefore, we extracted musical features from VGM in games from three sub-genres of Role-Playing Games (RPG), and then hypothesized how different musical features are correlated to the perceptions and portrayals of each genre. This observed correlation may be used to further suggest such features are relevant to the expected storytelling elements or play mechanics associated with the sub-genre.

Sara Çebi

The research examines the use of waste materials as a teaching resource in art classes. Third year students of Trabzon University Faculty of Fine Arts and Design, Department of Painting collected and sorted textile wastes randomly thrown into the environment and transformed these materials into artworks by wood printing method in the school's printing workshop. In this study, which adopted exploratory, experimental and descriptive research methods, 12 artworks were analyzed. The study reveals the effects of instructional resources on classroom atmosphere, student performance and shows that improper management of textile waste contributes to environmental pollution with aims to draw attention to the effects of textile waste for increase environmental aesthetics and awareness in society. Within the scope of the Applied Workshop II course, students transformed textile wastes collected from the environment into works of art with wood printing method. Students were informed about the wood printing technique and the place of recycling in art, and in the light of this information, they transformed fabric wastes into artistic compositions. This process contributed to the students' practical application of their theoretical knowledge and environmental awareness. The works were designed according to the color element and the principle of balance. Artists use colors to describe and depict the subject. The principle of balance is important for a work to be clear and harmonious. Students were asked to create their compositions according to these elements. Students were encouraged to participate in comments and criticism and a program was organized to discuss art production together.

Markus Virtanen

This article explores the media representation of Finnish-born composer Ann-Elise Hannikainen in the Spanish media during the early 1970s, focusing on the gender dynamics and the influence of the male gaze on her public image. Despite the presence of numerous female composers in Spain at the time, Hannikainen’s and Valencia-based Matilde Salvador’s works were among the few by women featured by Spanish orchestras in the 1970s. This study aims to understand how Hannikainen’s gender intersected with various aspects of her identity, such as age, appearance, social class, family background, education, and nationality, in the critiques and other texts related to her orchestral piece Anerfálicas premiered in Valencia in 1973. The methodology employs resistant reading by Judith Fetterley to analyse how gender and the male gaze shaped the discourse around Hannikainen’s work, underscoring the necessity of a feminist perspective in musicology that acknowledges the contributions of women composers and challenges the traditional narratives of music history. Additionally, by contrasting Hannikainen’s media image with that of Salvador, the article reveals that Hannikainen’s gender not only shaped her public image through descriptions of her appearance and familial relations but also affected the depth of authorship and artistic integrity attributed to her work, often overshadowing her professional credentials and accomplishments. This gendered narrative extended to the way influential figures, such as Hannikainen’s teacher Ernesto Halffter, represented Hannikainen.

Gyehun Go, Satbyul Han, Ahyeon Choi et al.

Recent advances in text-to-music (TTM) generation have enabled controllable and expressive music creation using natural language prompts. However, the emotional fidelity of TTM systems remains largely underexplored compared to human preference or text alignment. In this study, we introduce AImoclips, a benchmark for evaluating how well TTM systems convey intended emotions to human listeners, covering both open-source and commercial models. We selected 12 emotion intents spanning four quadrants of the valence-arousal space, and used six state-of-the-art TTM systems to generate over 1,000 music clips. A total of 111 participants rated the perceived valence and arousal of each clip on a 9-point Likert scale. Our results show that commercial systems tend to produce music perceived as more pleasant than intended, while open-source systems tend to perform the opposite. Emotions are more accurately conveyed under high-arousal conditions across all models. Additionally, all systems exhibit a bias toward emotional neutrality, highlighting a key limitation in affective controllability. This benchmark offers valuable insights into model-specific emotion rendering characteristics and supports future development of emotionally aligned TTM systems.

Zhaokai Wang, Chenxi Bao, Le Zhuo et al.

Vision-to-music Generation, including video-to-music and image-to-music tasks, is a significant branch of multimodal artificial intelligence demonstrating vast application prospects in fields such as film scoring, short video creation, and dance music synthesis. However, compared to the rapid development of modalities like text and images, research in vision-to-music is still in its preliminary stage due to its complex internal structure and the difficulty of modeling dynamic relationships with video. Existing surveys focus on general music generation without comprehensive discussion on vision-to-music. In this paper, we systematically review the research progress in the field of vision-to-music generation. We first analyze the technical characteristics and core challenges for three input types: general videos, human movement videos, and images, as well as two output types of symbolic music and audio music. We then summarize the existing methodologies on vision-to-music generation from the architecture perspective. A detailed review of common datasets and evaluation metrics is provided. Finally, we discuss current challenges and promising directions for future research. We hope our survey can inspire further innovation in vision-to-music generation and the broader field of multimodal generation in academic research and industrial applications. To follow latest works and foster further innovation in this field, we are continuously maintaining a GitHub repository at https://github.com/wzk1015/Awesome-Vision-to-Music-Generation.

Juan Carlos Martinez-Sevilla, Francesco Foscarin, Patricia Garcia-Iasci et al.

In this paper, we address the challenge of Optical Music Recognition (OMR) for handwritten jazz lead sheets, a widely used musical score type that encodes melody and chords. The task is challenging due to the presence of chords, a score component not handled by existing OMR systems, and the high variability and quality issues associated with handwritten images. Our contribution is two-fold. We present a novel dataset consisting of 293 handwritten jazz lead sheets of 163 unique pieces, amounting to 2021 total staves aligned with Humdrum **kern and MusicXML ground truth scores. We also supply synthetic score images generated from the ground truth. The second contribution is the development of an OMR model for jazz lead sheets. We discuss specific tokenisation choices related to our kind of data, and the advantages of using synthetic scores and pretrained models. We publicly release all code, data, and models.

Mathias Rose Bjare, Giorgia Cantisani, Marco Pasini et al.

We argue that training autoencoders to reconstruct inputs from noised versions of their encodings, when combined with perceptual losses, yields encodings that are structured according to a perceptual hierarchy. We demonstrate the emergence of this hierarchical structure by showing that, after training an audio autoencoder in this manner, perceptually salient information is captured in coarser representation structures than with conventional training. Furthermore, we show that such perceptual hierarchies improve latent diffusion decoding in the context of estimating surprisal in music pitches and predicting EEG-brain responses to music listening. Pretrained weights are available on github.com/CPJKU/pa-audioic.

Daniela Hřebíčková

Jingyue Huang, Ke Chen, Yi-Hsuan Yang

Managing the emotional aspect remains a challenge in automatic music generation. Prior works aim to learn various emotions at once, leading to inadequate modeling. This paper explores the disentanglement of emotions in piano performance generation through a two-stage framework. The first stage focuses on valence modeling of lead sheet, and the second stage addresses arousal modeling by introducing performance-level attributes. To further capture features that shape valence, an aspect less explored by previous approaches, we introduce a novel functional representation of symbolic music. This representation aims to capture the emotional impact of major-minor tonality, as well as the interactions among notes, chords, and key signatures. Objective and subjective experiments validate the effectiveness of our framework in both emotional valence and arousal modeling. We further leverage our framework in a novel application of emotional controls, showing a broad potential in emotion-driven music generation.

ماجدة محمد ماضي, إيمان جمال غزي, رانيا محمود بركات et al.

Research from experimental research aimed at using beads in the work of separate specific supplements as a manufacturer of small industries,The beads are part of the culture of every era in the world around the world and was used to decorate the human body of crown ,clothing ,bags and others.The human interest continued to decorate himself to follow the different times to this day and used multiple and unfamiliar ore in making the supplementation of clothing and increasing the beauty and glazing and gives a new creative form,The beads were used in raising the technical and aesthetic value of these supplements,small industries have a vital role in Egyptian economic development and contribute to making the Egyptian family dramatically ,The number of three bags and three gangs and five crowns are produced using cloth ,beads,metal wire ,tests and safety,After the production process ,these supplements were presented to a group of twenty professors to see their opinion by responding to a three-main axes and fourteen questions Beads to make separate dressing accessories as small industries. يعتبر البحث من الأبحاث التجريبية التي تهدف إلى استخدام الخرز في عمل بعض المكملات الملبسية المنفصلة كصناعة من الصناعات الصغيرة،فالخرز جزء من ثقافة كل عصر في شتي أنحاء العالم وكان يستخدم في تزيين الجسم الإنساني من تيجان وملابس وحقائب وغيرها ،واستمر اهتمام الإنسان بتزيين نفسه بتتابع العصور المختلفة حتي يومنا هذاواستخدمت خامات متعددة وغير مألوفة في صنع مكملات الملبس والتي تزيد من جمال ورونق الملابس وتعطي شكل ابداعي جديد واستخدم الخرز في رفع القيمة الفنية والجمالية لهذه المكملات ،والصناعات الصغيرة لها دوراً حيوياًفي التنمية الإقتصادية المصرية وتساهم في جعل الأسرة المصرية منتجة بشكل كبير ،وقد تم انتاج عدد3 شنط ،3 عصابة قدم،5 تيجان باستخدام القماش والخرز والسلك المعدني والفصوص والركامة وبعد عملية الانتاج تم عرض هذه المكملات علي مجموعة مكونة من (20)أستاذ متخصص لمعرفة رأيهم عن طريق الاستجابة علي استبيان مكون من ثلاثة محاور رئيسية، 14 سؤال، وقد أجمعت النتائج الي الاستفادة من الخرز في عمل مكملات ملبسية منفصلة كصناعات صغيرة .

Emmanouil Karystinaios, Francesco Foscarin, Florent Jacquemard et al.

This paper focuses on the nominal durations of musical events (notes and rests) in a symbolic musical score, and on how to conveniently handle these in computer applications. We propose the usage of a temporal unit that is directly related to the graphical symbols in musical scores and pair this with a set of operations that cover typical computations in music applications. We formalize this time unit and the more commonly used approach in a single mathematical framework, as semirings, algebraic structures that enable an abstract description of algorithms/processing pipelines. We then discuss some practical use cases and highlight when our system can improve such pipelines by making them more efficient in terms of data type used and the number of computations.

Maarten Grachten, Carlos Cancino-Chacón, Thassilo Gadermaier

This demo paper introduces partitura, a Python package for handling symbolic musical information. The principal aim of this package is to handle richly structured musical information as conveyed by modern staff music notation. It provides a much wider range of possibilities to deal with music than the more reductive (but very common) piano roll-oriented approach inspired by the MIDI standard. The package is an open source project and is available on GitHub.

Kinga Strycharz-Bogacz

Artykuł dotyczy Józefa Jaska z Milówki, wybitnej indywidualności w tradycyjnej kulturze Żywiecczyzny. Pochodził on z rodziny o tradycjach muzycznych. Śpiewać nauczył się od ojca, ludowego śpiewaka i matki, która znała wiele pieśni z kółka różańcowego. Józef Jasek miał sześcioro bardzo muzykalnych dzieci (m.in. córkę Irenę Golec, która jest matką Pawła i Łukasza Golców, popularnych współczesnych polskich muzyków). Był śpiewakiem pogrzebowym oraz przewodnikiem odpustowym i kalwaryjskim. Jako depozytariusz lokalnej religijnej tradycji muzycznej wypracował też swój indywidualny styl wykonawczy. Podczas badań terenowych przeprowadzonych w Milówce w 1973 roku przez pracowników i studentów Instytutu Muzykologii KUL zostały nagrane aż 92 śpiewy w jego wykonaniu, zdeponowane w Archiwum Muzycznego Folkloru Religijnego przy Katedrze Etnomuzykologii i Hymnologii KUL. Stanowią one niezwykle cenny zapis foniczny niematerialnego dziedzictwa kulturowego oraz interesujący materiał do studiów etnomuzykologicznych. Repertuar śpiewany przez Józefa Jaska obejmuje liczne gatunki religijnych śpiewów z żywej tradycji (pieśni adwentowe, kolędy i pastorałki, śpiewy wielkopostne i pasyjne, pieśni wielkanocne, maryjne, przygodne, do świętych, śpiewy pogrzebowe, pieśni kalwaryjskie, dziadowskie, do Opatrzności Boskiej), funkcjonujące w opisanej przez tego depozytariusza bogatej lokalnej obrzędowości dorocznej i rodzinnej.

Shreyan Chowdhury, Verena Praher, Gerhard Widmer

Music emotion recognition is an important task in MIR (Music Information Retrieval) research. Owing to factors like the subjective nature of the task and the variation of emotional cues between musical genres, there are still significant challenges in developing reliable and generalizable models. One important step towards better models would be to understand what a model is actually learning from the data and how the prediction for a particular input is made. In previous work, we have shown how to derive explanations of model predictions in terms of spectrogram image segments that connect to the high-level emotion prediction via a layer of easily interpretable perceptual features. However, that scheme lacks intuitive musical comprehensibility at the spectrogram level. In the present work, we bridge this gap by merging audioLIME -- a source-separation based explainer -- with mid-level perceptual features, thus forming an intuitive connection chain between the input audio and the output emotion predictions. We demonstrate the usefulness of this method by applying it to debug a biased emotion prediction model.

Filipe Calegario, João Tragtenberg, Giordano Cabral et al.

In this paper, we present an ongoing research project related to popularizing the mindset of building new digital musical instruments. We developed a physical kit and software intended to provide beginner users with the first grasp on the development process of a digital musical instrument. We expect that, by using the kit and the software, the users could experiment in a short period the various steps in developing a DMI such as physical structure, electronics, programming, mapping, and sound design. Our approach to popularizing the DMI development process is twofold: reducing the cognitive load for beginners by encapsulating technical details and lowering the costs of the kit by using simple components and open-source software. In the end, we expect that by increasing the interest of beginners in the building process of digital musical instruments, we could make the community of new interfaces for musical expression stronger.

K. V. Kysliuk

Розглянуто теоретико-методологічні особливості застосування культурологічного підходу до аналізу феномену візуального. Показано, що, на відміну від усталених наукових дисциплін і широких міждисциплінарних програм, культурологічний підхід має охоплювати значно ширший предмет — взаємозв’язок між візуальністю та культурою в глобальному чи локальному вимірах. Визначено, що його методологічним горизонтом водночас є інтерпретація специфіки візуальних конструкцій як вияву глибинних чи поверхових соціокультурних трендів й ідентифікація процесів зворотного впливу різних аспектів візуального на культуру. Аргументовано необхідність використання для цього як кількісних, так і якісних дослідницьких інструментів.

TJ Tsai, Kevin Ji

This paper studies composer style classification of piano sheet music images. Previous approaches to the composer classification task have been limited by a scarcity of data. We address this issue in two ways: (1) we recast the problem to be based on raw sheet music images rather than a symbolic music format, and (2) we propose an approach that can be trained on unlabeled data. Our approach first converts the sheet music image into a sequence of musical "words" based on the bootleg feature representation, and then feeds the sequence into a text classifier. We show that it is possible to significantly improve classifier performance by first training a language model on a set of unlabeled data, initializing the classifier with the pretrained language model weights, and then finetuning the classifier on a small amount of labeled data. We train AWD-LSTM, GPT-2, and RoBERTa language models on all piano sheet music images in IMSLP. We find that transformer-based architectures outperform CNN and LSTM models, and pretraining boosts classification accuracy for the GPT-2 model from 46\% to 70\% on a 9-way classification task. The trained model can also be used as a feature extractor that projects piano sheet music into a feature space that characterizes compositional style.

Germán Ruiz-Marcos

Stravinski's "The Rite of Spring" is one of the most well-known pieces from the classical contemporary music repertoire. However, its analysis has aroused different opinions within its construction and compositional foundations. In this sense, I here proposed my own manual analysis and a computational approach which aims to find a similar analysis, giving the opportunity of discovering new possible points of view and supplying the current deficiencies of the Musi"c Computing common analysis systems.

Minz Won, Sanghyuk Chun, Xavier Serra

Self-attention is an attention mechanism that learns a representation by relating different positions in the sequence. The transformer, which is a sequence model solely based on self-attention, and its variants achieved state-of-the-art results in many natural language processing tasks. Since music composes its semantics based on the relations between components in sparse positions, adopting the self-attention mechanism to solve music information retrieval (MIR) problems can be beneficial. Hence, we propose a self-attention based deep sequence model for music tagging. The proposed architecture consists of shallow convolutional layers followed by stacked Transformer encoders. Compared to conventional approaches using fully convolutional or recurrent neural networks, our model is more interpretable while reporting competitive results. We validate the performance of our model with the MagnaTagATune and the Million Song Dataset. In addition, we demonstrate the interpretability of the proposed architecture with a heat map visualization.