Evaluation of Drum Rhythmspace in a Music Production Environment

In modern computer-based music production, vast musical data libraries are essential. However, their presentation via subpar interfaces can hinder creativity, complicating the selection of ideal sequences. While low-dimensional space solutions have been suggested, their evaluations in real-world music production remain limited. In this study, we focus on Rhythmspace, a two-dimensional platform tailored for the exploration and generation of drum patterns in symbolic MIDI format. Our primary objectives encompass two main aspects: first, the evolution of Rhythmspace into a VST tool specifically designed for music production settings, and second, a thorough evaluation of this tool to ascertain its performance and applicability within the music production scenario. The tool’s development necessitated transitioning the existing Rhythmspace, which operates in Puredata and Python, into a VST compatible with Digital Audio Workstations (DAWs) using the JUCE(C++) framework. Our evaluation encompassed a series of experiments, starting with a composition test where participants crafted drum sequences followed by a listening test, wherein participants ranked the sequences from the initial experiment. The results show that Rhythmspace and similar tools are beneficial, facilitating the exploration and creation of drum patterns in a user-friendly and intuitive manner, and enhancing the creative process for music producers. These tools not only streamline the drum sequence generation but also offer a fresh perspective, often serving as a source of inspiration in the dynamic realm of electronic music production

A General Framework for Visualization of Sound Collections in Musical Interfaces

open access articleWhile audio data play an increasingly central role in computer-based music production, interaction with large sound collections in most available music creation and production environments is very often still limited to scrolling long lists of file names. This paper describes a general framework for devising interactive applications based on the content-based visualization of sound collections. The proposed framework allows for a modular combination of different techniques for sound segmentation, analysis, and dimensionality reduction, using the reduced feature space for interactive applications. We analyze several prototypes presented in the literature and describe their limitations. We propose a more general framework that can be used flexibly to devise music creation interfaces. The proposed approach includes several novel contributions with respect to previously used pipelines, such as using unsupervised feature learning, content-based sound icons, and control of the output space layout. We present an implementation of the framework using the SuperCollider computer music language, and three example prototypes demonstrating its use for data-driven music interfaces. Our results demonstrate the potential of unsupervised machine learning and visualization for creative applications in computer music

How Visualization Supports the Daily Work in Traditional Humanities on the Example of Visual Analysis Case Studies

Attempts to convince humanities scholars of digital approaches are met with resistance, often. The so-called Digitization Anxiety is the phenomenon that describes the fear of many traditional scientists of being replaced by digital processes. This hinders not only the progress of the scientific domains themselves – since a lot of digital potential is missing – but also makes the everyday work of researchers unnecessarily difficult. Over the past eight years, we have made various attempts to walk the tightrope between 'How can we help traditional humanities to exploit their digital potential?' and 'How can we make them understand that their expertise is not replaced by digital means, but complemented?' We will present our successful interdisciplinary collaborations: How they came about, how they developed, and the problems we encountered. In the first step, we will look at the theoretical basics, which paint a comprehensive picture of the digital humanities and introduces us to the topic of visualization. The field of visualization has shown a special ability: It manages to walk the tightrope and thus keeps digitization anxiety at bay, while not only making it easier for scholars to access their data, but also enabling entirely new research questions. After an introduction to our interdisciplinary collaborations with the Musical Instrument Museum of Leipzig University, as well as with the Bergen-Belsen Memorial, we will present a series of user scenarios that we have collected in the course of 13 publications. These show our cooperation partners solving different research tasks, which we classify using Brehmer and Munzner’s Task Classification. In this way, we show that we provide researchers with a wide range of opportunities: They can answer their traditional research questions – and in some cases verify long-standing hypotheses about the data for the first time – but also develop their own interest in previously impossible, new research questions and approaches. Finally, we conclude our insights on individual collaborative ideas with perspectives on our newest projects. These have risen from the growing interest of collaborators in the methods we deliver. For example, we get insights into the music of real virtuosos of the 20th century. The necessary music storage media can be heard for the first time through digital tools without risking damage to the old material. In addition, we can provide computer-aided analysis capabilities that help musicologists in their work. In the course of the visualization project at the Bergen-Belsen memorial, we will see that what was once a small diary project has grown into a multimodal and international project with institutions of culture and science from eight countries. This is dedicated not only to the question of preserving cultural objects from Nazi persecution contexts but also to modern ways of disseminating and processing knowledge around this context. Finally, we will compile our experience and accumulated knowledge in the form of problems and challenges at the border between computer science and traditional humanities. These will serve as preparation and assistance for future and current interested parties of such interdisciplinary collaborative project

Vocal imitation for query by vocalisation

PhD ThesisThe human voice presents a rich and powerful medium for expressing sonic ideas such as musical sounds. This capability extends beyond the sounds used in speech, evidenced for example in the art form of beatboxing, and recent studies highlighting the utility of vocal imitation for communicating sonic concepts. Meanwhile, the advance of digital audio has resulted in huge libraries of sounds at the disposal of music producers and sound designers. This presents a compelling search problem: with larger search spaces, the task of navigating sound libraries has become increasingly difficult. The versatility and expressive nature of the voice provides a seemingly ideal medium for querying sound libraries, raising the question of how well humans are able to vocally imitate musical sounds, and how we might use the voice as a tool for search. In this thesis we address these questions by investigating the ability of musicians to vocalise synthesised and percussive sounds, and evaluate the suitability of different audio features for predicting the perceptual similarity between vocal imitations and imitated sounds. In the first experiment, musicians were tasked with imitating synthesised sounds with one or two time–varying feature envelopes applied. The results show that participants were able to imitate pitch, loudness, and spectral centroid features accurately, and that imitation accuracy was generally preserved when the imitated stimuli combined two, non-necessarily congruent features. This demonstrates the viability of using the voice as a natural means of expressing time series of two features simultaneously. The second experiment consisted of two parts. In a vocal production task, musicians were asked to imitate drum sounds. Listeners were then asked to rate the similarity between the imitations and sounds from the same category (e.g. kick, snare etc.). The results show that drum sounds received the highest similarity ratings when rated against their imitations (as opposed to imitations of another sound), and overall more than half the imitated sounds were correctly identified with above chance accuracy from the imitations, although this varied considerably between drum categories. The findings from the vocal imitation experiments highlight the capacity of musicians to vocally imitate musical sounds, and some limitations of non– verbal vocal expression. Finally, we investigated the performance of different audio features as predictors of perceptual similarity between the imitations and imitated sounds from the second experiment. We show that features learned using convolutional auto–encoders outperform a number of popular heuristic features for this task, and that preservation of temporal information is more important than spectral resolution for differentiating between the vocal imitations and same–category drum sounds

16th Sound and Music Computing Conference SMC 2019 (28–31 May 2019, Malaga, Spain)

The 16th Sound and Music Computing Conference (SMC 2019) took place in Malaga, Spain, 28-31 May 2019 and it was organized by the Application of Information and Communication Technologies Research group (ATIC) of the University of Malaga (UMA). The SMC 2019 associated Summer School took place 25-28 May 2019. The First International Day of Women in Inclusive Engineering, Sound and Music Computing Research (WiSMC 2019) took place on 28 May 2019. The SMC 2019 TOPICS OF INTEREST included a wide selection of topics related to acoustics, psychoacoustics, music, technology for music, audio analysis, musicology, sonification, music games, machine learning, serious games, immersive audio, sound synthesis, etc

Intelligent Tools for Drum Loop Retrieval and Generation

Large libraries of musical data are an increasingly common feature of contemporary computer-based music production practice, with producers often relying heavily on large, curated libraries of data such as loops and samples when making tracks. Drum loop libraries are a particularly common type of library in this context. However, their typically large size, coupled with often poor user interfaces means navigating and exploring them in a fast, easy and enjoyable way is not always possible. Additionally, writing a drum part for a whole track out of many drum loops can be a laborious process, requiring manually editing of many drum loops. The aim of this thesis is to contribute novel techniques based on Music Information Retrieval (MIR) and machine learning that make the process of writing drum tracks using drum loops faster, easier and more enjoyable. We primarily focus on tools for drum loop library navigation and exploration, with additional work on assistive generation of drum loops. We contribute proof-of-concept and prototype tools, Groove Explorer and Groove Explorer 2, for drum loop library exploration based on an interface applying similarity-based visual arrangement of drum loops. Work on Groove Explorer suggested that there were limitations in the existing state-of-the-art approaches to drum loop similarity modelling that must be addressed for tools such as ours to be successful. This was verified via a perceptual study, which identified possible areas of improvement in similarity modelling. Following this, we develop and evaluate a set of novel models for drum loop analysis that capture rhythmic structure and the perceptually relevant qualities of microtiming. Drawing from this, a new approach to drum loop similarity modelling was verified in context as part of Groove Explorer 2, which we evaluated via a user study. The results indicated that our approach could make drum loop library exploration faster, easier and more enjoyable. We finally present an automatic drum loop generation system, jaki, that uses a novel approach for drum loop generation according to user constraints, that could extend Groove Explorer 2 as a drum loop editing and composition tool. Combined, these two systems could offer an end-to-end solution to improved writing of drum tracks

Third International Conference on Technologies for Music Notation and Representation TENOR 2017

The third International Conference on Technologies for Music Notation and Representation seeks to focus on a set of specific research issues associated with Music Notation that were elaborated at the first two editions of TENOR in Paris and Cambridge. The theme of the conference is vocal music, whereas the pre-conference workshops focus on innovative technological approaches to music notation

E-Tickets and Advanced Technologies for Efficient Construction Inspections

The Kentucky Transportation Cabinet (KYTC), like many state transportation agencies, has seen demand for high-quality infrastructure skyrocket even as it endures reductions in staff numbers. To mitigate the effects of declining staff and bolster construction efficiency, the Cabinet has experimented with a variety of e-construction technologies, the goal of which are to abolish paper-based workflows and improve project-site monitoring activities. This research investigated the performance of three e-construction technologies on KYTC pilot projects — e-ticketing, paver mounted thermal profilers, and intelligent compaction. E-ticketing reduced the amount of time needed to retrieve material tickets and facilitated comparisons of theoretical tonnages to actual tonnages. Inspectors also reduced their exposure to hazardous jobsite conditions through the use of e-ticketing, while contractors strengthened their operational efficiencies. Paver mounted thermal profilers collected temperature data whose accuracy was not significantly different from temperature data gathered using conventional infrared guns. The spatially continuous data generated by profilers can aid in later monitoring of pavement performance and can be used to perform forensic investigations of pavement distress. Although other state transportation agencies have adopted intelligent compaction with considerable success, it produced inaccurate data on asphalt temperature and roller passes. Several factors may have contributed to this unexpected result, such as poor communication between project stakeholders and incorrectly executed equipment setup. The three technologies could potentially be adopted on a more widespread basis; however, it is critical to offer adequate training to equipment and software users, ensure that project stakeholders coordinate and communicate with one another, and be conscientious in the deployment and management of equipment