Deleuze and music : a creative approach to the study of music

The work of Gilles Deleuze has influenced an increasing number of music scholars and practicing musicians, particularly those interested in experimental, electronic and popular music. This is despite the notoriously complex nature of his writings, and the specialised theoretical vocabulary that he employs. This thesis both demystifies some of the key terms and concepts of this vocabulary, before demonstrating how Deleuze’s ideas may be put to work in new and fruitful ways; this is achieved with specific reference to the relationships that music has with thought, time and machines. In Chapter 1, Deleuze’s understanding of the power of thought is examined, in particular his approach to communication, transcendence and immanence, and the “powers of thought.” Each of these concepts helps us to understand Deleuze’s work within broad problem of how to think about music immanently: that is, how to maintain that thought and music are both immanent aspects of life and experience. Chapter 2 examines time within a Deleuzian framework, linking his work on cinema with the concept of the “refrain”; both of these areas prove crucial to his understanding of music, as seen in Deleuze’s approach to the work of Varese, Messiaen, and Boulez. In addition, Deleuze’s understanding of time proves fruitful in examining various aspects of music production, as seen in contemporary electronic dance music. Finally, Chapter 3 looks at the concept of the machine, as developed by Deleuze and Guattari, with reference to the sorts of “machinic” connections that a Deleuzian approach encourages us to seek out in music. Once again, examples from contemporary electronic music are presented, in relation to the notions of becoming and subjectivity. Throughout these chapters, Deleuze’s broad understanding of philosophy as the “creation of concepts” is deployed. This means introducing new ideas and specific types of music that encourage creative and novel engagements with the study of music.

Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

The design and development of music eScape: A new iPhone mood management music App

The physical, emotional, educational and social developmental challenges of adolescence can be associated with high levels of emotional vulnerability. Thus, the development of effective emotion-regulation strategies is crucial during this time period. Young people commonly use music to identify, express and regulate their emotions. Modern mobile technology provides an engaging, easily accessible means of assisting young people through music. A systematic contextual review identified 20 iPhone applications addressing emotions through music and two independent raters, using the Mobile App Rating Scale (MARS), evaluated the quality of the apps. Their characteristics, key features and overall quality will be presented. Three participatory design workshops (N=13, 6 males, 7 females; age 15-25) were conducted to explore young people’s use of music to enhance wellbeing. Young people were also asked to trial existing mood and music apps and to conceptualise their ultimate mood targeting music application. A thematic analysis of the participatory design workshops content identified the following music affect-regulation strategies: relationship building, modifying cognitions, modifying emotions, and immersing in emotions. The application of the key learnings from the mobile app review and participatory design workshops and the design and development of the music eScape app were presented and implications for future research was discussed.

Automatic inference of driving task demand from visual cues of emotion and attention

Sensing the mental, physical and emotional demand of a driving task is of primary importance in road safety research and for effectively designing in-vehicle information systems (IVIS). Particularly, the need of cars capable of sensing and reacting to the emotional state of the driver has been repeatedly advocated in the literature. Algorithms and sensors to identify patterns of human behavior, such as gestures, speech, eye gaze and facial expression, are becoming available by using low cost hardware: This paper presents a new system which uses surrogate measures such as facial expression (emotion) and head pose and movements (intention) to infer task difficulty in a driving situation. 11 drivers were recruited and observed in a simulated driving task that involved several pre-programmed events aimed at eliciting emotive reactions, such as being stuck behind slower vehicles, intersections and roundabouts, and potentially dangerous situations. The resulting system, combining face expressions and head pose classification, is capable of recognizing dangerous events (such as crashes and near misses) and stressful situations (e.g. intersections and way giving) that occur during the simulated drive.

A convolutional neural network for automatic analysis of aerial imagery

This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.

Young people's uses of music for well-being

The period between 15 and 25 years is characterised by much personal change and is the peak age of onset of mental health problems. This prompts an interest in everyday strategies that young people might use to support their well-being. Music use is the preferred leisure activity among young people yet little is known about how music is linked to well-being in this population. This study aimed to develop and test a model of the relationships between young people’s use of music and their well-being, drawing on theories from the music psychology and clinical psychology fields. A qualitative analysis of transcripts from focus groups with 11 participants aged 15–25 years revealed four ways in which music listening links with well-being: relationship building, modifying emotions, modifying cognitions and emotional immersion. These linking variables were operationalised using questionnaire scores and tested on a new sample of 107 young people. Results of a multiple mediation analysis revealed that music listening was significantly related to all four linking variables, but not directly related to well-being as measured by the Mental Health Continuum. Nevertheless, the four linking variables indirectly mediated the effect of music listening on social wellbeing. The findings are consistent with earlier research on the role of music in emotion regulation and social connection although there are clearly other factors involved in determining young peoples’ well-being. These findings will help inform music-based interventions for young people.

Automatic clustering of white matter fibers in brain diffusion mri with an application to genetics

To understand factors that affect brain connectivity and integrity, it is beneficial to automatically cluster white matter (WM) fibers into anatomically recognizable tracts. Whole brain tractography, based on diffusion-weighted MRI, generates vast sets of fibers throughout the brain; clustering them into consistent and recognizable bundles can be difficult as there are wide individual variations in the trajectory and shape of WM pathways. Here we introduce a novel automated tract clustering algorithm based on label fusion - a concept from traditional intensity-based segmentation. Streamline tractography generates many incorrect fibers, so our top-down approach extracts tracts consistent with known anatomy, by mapping multiple hand-labeled atlases into a new dataset. We fuse clustering results from different atlases, using a mean distance fusion scheme. We reliably extracted the major tracts from 105-gradient high angular resolution diffusion images (HARDI) of 198 young normal twins. To compute population statistics, we use a pointwise correspondence method to match, compare, and average WM tracts across subjects. We illustrate our method in a genetic study of white matter tract heritability in twins.

Will algorithmic playlist curation be the end of music stardom?

It is 2015 and there are no indications that the relentless digital transformation of the music economy is about to slow down. Rather, the music economy continues to rapidly reinvent itself and industry powers, positions and practices that were redefined only a few years ago are being questioned once again. This paper examines the most recent changes of the music economy as it moves from a product-based towards an access-based logic. The paper starts out by recognising the essential role of technology in the evolution of the music economy. It then moves on to a discussion about the rise of so-called access-based music business models and points out some of the controversies and debates that are associated with these models and online services. With this as a background the paper explores how access-based music services and the algorithmically curated playlists developed by these services transform the relationship between artists, music and fans and challenges the music industrial power relationships and established industry practices once again.

Impact of automatic region-of-interest coding on perceived quality in mobile video

At present, the most reliable method to obtain end-user perceived quality is through subjective tests. In this paper, the impact of automatic region-of-interest (ROI) coding on perceived quality of mobile video is investigated. The evidence, which is based on perceptual comparison analysis, shows that the coding strategy improves perceptual quality. This is particularly true in low bit rate situations. The ROI detection method used in this paper is based on two approaches: - (1) automatic ROI by analyzing the visual contents automatically, and; - (2) eye-tracking based ROI by aggregating eye-tracking data across many users, used to both evaluate the accuracy of automatic ROI detection and the subjective quality of automatic ROI encoded video. The perceptual comparison analysis is based on subjective assessments with 54 participants, across different content types, screen resolutions, and target bit rates while comparing the two ROI detection methods. The results from the user study demonstrate that ROI-based video encoding has higher perceived quality compared to normal video encoded at a similar bit rate, particularly in the lower bit rate range.

Microprocessor-controlled automatic focusing of a lens

Maximum intensity contrast has been used as a measure of lens defocus. A photodiode array under the control of 8085 microprocessor is used to measure the maximum intensity contrast and to position the lens for best focus. The lens is moved by a stepper motor under processor control at a speed of 350 to 500 steps/s. At this speed, focusing time was found to be between 5 and 8 s. Under coherent illuminating conditions, an accuracy of ± 50 μm has been achieved.

The Development of the Concept of Music Education in Estonian Kindergartens, 1905-2008 : a Historical–Critical Overview

The dissertation focuses on the development of music education in Estonian kindergartens and the factors influencing it, analysed in the historical perspective relying on post-positivist paradigm. The study is based on the factors and subjects’ views on kindergarten music education from 1905 to 2008, recorded in written sources or ascertained by means of questionnaire and interview. The dissertation deals with music’s functions, music education in retrospective, factors influencing child’s musical aptitude and development and teacher’s role in it through the prism of history. The formation of Estonian kindergarten music education and the phenomenon of its development have been researched by stages: the first manifestations of music in kindergarten in 1905 - 1940; the formation of the concept of music education in 1941 - 1967 and the application of a unified system in 1968 - 1990. The work also outlines innovative trends in music education at the end of the last millennium and the beginning of this century, in 1991 - 2008. The study relies on a combined design and an analysis of historical archival material and empirical data. The empirical part of the study is based on the questionnaire (n=183) and interviews (n=18) carried out with kindergarten music teachers. The data has been analysed using both qualitative and quantitative methods. The subject of the research is the content and activity types of kindergarten music education and the role of music teacher in their implementation. The study confirmed that fundamental changes took place in Estonian kindergarten music education due to the change in political power in the 1940s. Following the example of the Soviet system of education, music in kindergarten became an independent music educational orientation and the position of a professionally trained music teacher was established (1947). It was also confirmed that in the newly independent Estonian Republic under the influence of innovative trends a new paradigm of music education arose from the traditional singing-centred education towards the more balanced use of music activity types (attaching importance to the child-centred approach, an increase in the number and variety of activity types). The most important conclusions made in the dissertation are that there has been improvement and development deriving from contemporary trends in the clear concept that has evolved in Estonian kindergarten music education over a century; professionally trained music teachers have had a crucial role in shaping it; and kindergarten music education is firmly positioned as a part of preschool education in Estonian system of education. Key words: early childhood music education, history of music education, kindergarten music education, early childhood music teachers

International Journal of Music Business Research. Special Issue on the Australasian Music Economy

The music business is one of the most international of all the cultural industries. Music, industry practices, and people travel easily across country borders and the major music companies are dominating national music markets across the globe. However, at the same time the music industries in different countries are very idiosyncratic. Music is an ingrained part of a country’s history, its culture and heritage. One aspect of this idiosyncrasy is related to how creatives, audiences and music organizations are affected by and is able to take advantage of the ongoing digitization of society.

Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis

Early identification of diabetic foot complications and their precursors is essential in preventing their devastating consequences, such as foot infection and amputation. Frequent, automatic risk assessment by an intelligent telemedicine system might be feasible and cost effective. Infrared thermography is a promising modality for such a system. The temperature differences between corresponding areas on contralateral feet are the clinically significant parameters. This asymmetric analysis is hindered by (1) foot segmentation errors, especially when the foot temperature and the ambient temperature are comparable, and by (2) different shapes and sizes between contralateral feet due to deformities or minor amputations. To circumvent the first problem, we used a color image and a thermal image acquired synchronously. Foot regions, detected in the color image, were rigidly registered to the thermal image. This resulted in 97.8% ± 1.1% sensitivity and 98.4% ± 0.5% specificity over 76 high-risk diabetic patients with manual annotation as a reference. Nonrigid landmark-based registration with Bsplines solved the second problem. Corresponding points in the two feet could be found regardless of the shapes and sizes of the feet. With that, the temperature difference of the left and right feet could be obtained.