Search Results (12)

Objective: To provide a state of the art on the use, role, and function of music during psychedelic-assisted therapy (PAT) with ayahuasca. Introduction: Ayahuasca is a medicinal brew with psychoactive qualities used by indigenous communities throughout the Amazon region, and music is deemed crucial during ayahuasca experiences. In PAT, music forms part of the set and setting, but it lacks systematization and is poorly explored in the scientific literature. Inclusion criteria: Published literature in English, Spanish, and Portuguese, focusing on conceptualizing, defining, or describing the use, role, and/or function of music in PAT sessions with ayahuasca. Methods: This review follows the JBI methodology for scoping reviews. We will search Web of Science, Scopus, Google Scholar, and PubMed databases without year restrictions, and a hand search of articles will be performed. Two reviewers will assess titles/abstracts, followed by independent reviews of included full texts. An Excel data extraction sheet will be used to tabulate the information. The findings will be presented narratively, including respective tables or figures. If feasible, a potential theoretical framework for the use, role, and function of music during PAT with ayahuasca will be outlined, including implications for future research and clinical practice. Full article

Sheet music is one of the most efficient methods for storing music. Meanwhile, a large amount of sheet music-image data is stored in paper form, but not in a computer-readable format. Therefore, digitizing sheet music is an essential task, such that the encoded music object could be effectively utilized for tasks such as editing or playback. Although there have been a few studies focused on recognizing sheet music images with simpler structures—such as monophonic scores or more modern scores with relatively simple structures, only containing clefs, time signatures, key signatures, and notes—in this paper we focus on the issue of classical sheet music containing dynamics symbols and articulation signs, more than only clefs, time signatures, key signatures, and notes. Therefore, this study augments the data from the GrandStaff dataset by concatenating single-line scores into multi-line scores and adding various classical music dynamics symbols not included in the original GrandStaff dataset. Given a full-scale piano score in pages, our approach first applies three YOLOv8 models to perform the three tasks: 1. Converting a full page of sheet music into multiple single-line scores; 2. Recognizing the classes and absolute positions of dynamics symbols in the score; and 3. Finding the relative positions of dynamics symbols in the score. Then, the identified dynamics symbols are removed from the original score, and the remaining score serves as the input into a Convolutional Recurrent Neural Network (CRNN) for the following steps. The CRNN outputs KERN notation (KERN, a core pitch/duration representation for common practice music notation) without dynamics symbols. By combining the CRNN output with the relative and absolute position information of the dynamics symbols, the final output is obtained. The results show that with the assistance of YOLOv8, there is a significant improvement in accuracy. Full article

Music transcription is the process of transforming recorded sound of musical performances into symbolic representations such as sheet music or MIDI files. Extensive research and development have been carried out in the field of music transcription and technology. This comprehensive review paper surveys the diverse methodologies, techniques, and advancements that have shaped the landscape of music transcription. The paper outlines the significance of music transcription in preserving, analyzing, and disseminating musical compositions across various genres and cultures. It also provides a historical perspective by tracing the evolution of music transcription from traditional manual methods to modern automated approaches. It also highlights the challenges in transcription posed by complex singing techniques, variations in instrumentation, ambiguity in pitch, tempo changes, rhythm, and dynamics. The review also categorizes four different types of transcription techniques, frame-level, note-level, stream-level, and notation-level, discussing their strengths and limitations. It also encompasses the various research domains of music transcription from general melody extraction to vocal melody, note-level monophonic to polyphonic vocal transcription, single-instrument to multi-instrument transcription, and multi-pitch estimation. The survey further covers a broad spectrum of music transcription applications in music production and creation. It also reviews state-of-the-art open-source as well as commercial music transcription tools for pitch estimation, onset and offset detection, general melody detection, and vocal melody detection. In addition, it also encompasses the currently available python libraries that can be used for music transcription. Furthermore, the review highlights the various open-source benchmark datasets for different areas of music transcription. It also provides a wide range of references supporting the historical context, theoretical frameworks, and foundational concepts to help readers understand the background of music transcription and the context of our paper. Full article

The piano fingering annotation task refers to assigning finger labels to notes in piano sheet music. Good fingering helps improve the smoothness and musicality of piano performance. In this paper, we propose a method for automatically generating piano fingering using a model-based reinforcement learning algorithm. We treat fingering annotation as a partial constraint combinatorial optimization problem and establish an environment model for the piano performance process based on prior knowledge. We design a reward function based on the principle of minimal motion and use reinforcement learning algorithms to decide the optimal fingering combinations. Our innovation lies in establishing a more realistic environment model and adopting a model-based reinforcement learning approach, compared to model-free methods, to enhance the utilization of samples. We also propose a music score segmentation method to parallelize the fingering annotation task. The experimental section shows that our method achieves good results in eliminating physically impossible fingerings and reducing the amount of finger motion required in piano performance. Full article

The popularity of social media is continuously growing, as it endeavors to bridge the gap in communication between individuals. YouTube, one of the most well-known social media platforms with millions of users, stands out due to its remarkable ability to facilitate communication through the exchange of video content. Despite its primary purpose being entertainment, YouTube also offers individuals the valuable opportunity to learn from its vast array of educational content. The primary objective of this study is to explore the sentiments of YouTube learners by analyzing their comments on educational YouTube videos. A total of 167,987 comments were extracted and processed from educational YouTube channels through the YouTube Data API and Google Sheets. Lexicon-based sentiment analysis was conducted using two different methods, VADER and TextBlob, with the aim of detecting the prevailing sentiment. The sentiment analysis results revealed that the dominant sentiment expressed in the comments was neutral, followed by positive sentiment, while negative sentiment was the least common. VADER and TextBlob algorithms produced comparable results. Nevertheless, TextBlob yielded higher scores in both positive and negative sentiments, whereas VADER detected a greater number of neutral statements. Furthermore, the Latent Dirichlet Allocation (LDA) topic clustering outcomes shed light on various video attributes that potentially influence viewers’ experiences. These attributes included animation, music, and the conveyed messages within the videos. These findings make a significant contribution to ongoing research efforts aimed at understanding the educational advantages of YouTube and discerning viewers’ preferences regarding video components and educational topics. Full article

The recognition of printed music sheets in camera-based realistic scenarios is a novel research branch of optical music recognition (OMR). However, special factors in realistic scenarios, such as uneven lighting distribution and curvature of staff lines, can have adverse effects on OMR models designed for digital music scores. This paper proposes a stave-aware method based on object detection to recognize monophonic printed sheet music in camera-based scenarios. By detecting the positions of staff lines, we improve the accuracy of note pitch effectively. In addition, we present the Camera Printed Music Staves (CPMS) dataset, which consists of labels and images captured by mobile phones under different angles and lighting conditions in realistic scenarios. We compare our method after training on different datasets with a sequence recognition method called CRNN-CTC on the test set of the CPMS dataset. The results show that the accuracy, robustness, and data dependency of our method perform better. Full article

Legibility can seem as similar to the quintessence of musical notation, without which any attempt at musical inscription has fundamentally no purpose. Nevertheless, the visual culture of the English Renaissance is full of surviving examples that feature music books that are, fundamentally, illegible. Such instances are not useless, but rather shed vital light on the concerns of the visual culture of the English Renaissance, as well as what representation meant to the people who originally created and viewed these objects. What does it mean to include sheet music that merely looks similar to, but does not manifest, as legible notation? When does an object lose its semantic value? When do writing, notation, and signification pull lose from their seams and cease to be meaningful? Through the lens of a trio of objects (Four Children Making Music by the Master of the Countess of Warwick, an anonymous furnishing panel from Hardwick Hall, and a wall painting from High Street, Thame) that feature partially, or tantalizingly, legible musical notation, this paper seeks to explore the ramifications of visually depicting things that are and are not readable. Such objects have a graphic eloquence beyond the simple equation of sign and signified. Ultimately, entertaining the concept of illegible music notation within visual art objects as a deliberate stylistic choice, I argue that we can greatly enhance our understanding of what the notes on the page could mean in the English Renaissance. Full article

This paper studies the problem of identifying piano music in various modalities using a single, unified approach called marketplace fingerprinting. The key defining characteristic of marketplace fingerprinting is choice: we consider a broad range of fingerprint designs based on a generalization of standard n-grams, and then select the fingerprint designs at runtime that are best for a specific query. We show that the large-scale retrieval problem can be framed as an economics problem in which a consumer and a store interact. In our analogy, the runtime search is like a consumer shopping in the store, the items for sale correspond to fingerprints, and purchasing an item corresponds to doing a fingerprint lookup in the database. Using basic principles of economics, we design an efficient marketplace in which the consumer has many options and adopts a rational buying strategy that explicitly considers the cost and expected utility of each item. We evaluate our marketplace fingerprinting approach on four different sheet music retrieval tasks involving sheet music images, MIDI files, and audio recordings. Using a database containing approximately 375,000 pages of sheet music, our method is able to achieve $0.91$ mean reciprocal rank with sub-second average runtime on cell phone image queries. On all four retrieval tasks, the marketplace method substantially outperforms previous methods while simultaneously reducing average runtime. We present comprehensive experimental results, as well as detailed analyses to provide deeper intuition into system behavior. Full article

We explore the structural similarities in three different languages, first in the protein language whose primary letters are the amino acids, second in the musical language whose primary letters are the notes, and third in the poetry language whose primary letters are the alphabet. For proteins, the non local (secondary) letters are the types of foldings in space ($\alpha$-helices, $\beta$-sheets, etc.); for music, one is dealing with clear-cut repetition units called musical forms and for poems the structure consists of grammatical forms (names, verbs, etc.). We show in this paper that the mathematics of such secondary structures relies on finitely presented groups $f_p$ on r letters, where r counts the number of types of such secondary non local segments. The number of conjugacy classes of a given index (also the number of graph coverings over a base graph) of a group $f_p$ is found to be close to the number of conjugacy classes of the same index in the free group $F_{r-1}$ on $r-1$ generators. In a concrete way, we explore the group structure of a variant of the SARS-Cov-2 spike protein and the group structure of apolipoprotein-H, passing from the primary code with amino acids to the secondary structure organizing the foldings. Then, we look at the musical forms employed in the classical and contemporary periods. Finally, we investigate in much detail the group structure of a small poem in prose by Charles Baudelaire and that of the Bateau Ivre by Arthur Rimbaud. Full article

This paper describes the relationship between a strong architectural vision that is difficult to balance, and user expectations in terms of acoustics. The focus is on the use of corten steel as the dominant finishing material on façades and interiors to achieve an expressive, symbolic message through program-based design. The architectural premises justifying the adopted solutions are presented, especially the universality and homogeneity of the material. Against this background, the influence of corten steel on the acoustics of the two largest rooms of the European Solidarity Center, which are the winter garden and the multi-purpose hall, was discussed. Remedial steps have been taken to reduce the greatest acoustic inconveniences resulting from the widespread use of metal sheet as a finishing material in rooms, i.e., excessive reverberation and a low degree of sound dispersion. A positive result for the acoustic conditions achieved in the winter garden was the presentation of a large body of classical music in the building. Full article

This article studies a composer style classification task based on raw sheet music images. While previous works on composer recognition have relied exclusively on supervised learning, we explore the use of self-supervised pretraining methods that have been recently developed for natural language processing. We first convert sheet music images to sequences of musical words, train a language model on a large set of unlabeled musical “sentences”, initialize a classifier with the pretrained language model weights, and then finetune the classifier on a small set of labeled data. We conduct extensive experiments on International Music Score Library Project (IMSLP) piano data using a range of modern language model architectures. We show that pretraining substantially improves classification performance and that Transformer-based architectures perform best. We also introduce two data augmentation strategies and present evidence that the model learns generalizable and semantically meaningful information. Full article

The modern history of disability, and of speech impediments in particular, has largely been written as one of medical discourse and (more recently) of social and cultural imaginations. The pathology of speech appears as an embodied, but ultimately intangible, issue due to the transient nature of sound itself. Once produced, it disappears, and seems to escape memory. In this text, stammering is approached as an object of material history. Drawing on the “paper trail” left by medical experts, popular entertainers and a handful of stammerers’ experiences, this paper examines the ways in which stammering was made material in the nineteenth century. The impediment not only provided (pseudo) medical actors with a lucrative market for various curative objects and practices, but also propelled the (sheet-)music business. Stammerers themselves appear in this story of materialization and market as both agents and objects. The cheap self-cures, medical manuals, sheet music and (later) recordings that were produced not only for, but also by, them, show how easily the impediment was aligned with the modern consumer’s identity and how the persona of the stammerer was, ultimately, lodged in the Western collective memory in very material ways. Full article