Where’s the transformation? Unlocking the potential of technology-enhanced assessment

This study provides insight into technology-enhanced assessment (TEA) in diverse higher education contexts. The effectiveness of using technology for assessment in higher education is still equivocal, particularly in regard to evidence of improvements in student learning. This empirical research explores the affordances that technology offers to assessment for transforming student learning. A systematic literature review, guided by an analytic survey tool, was used to identify and interrogate recent scholarly articles published in 19 international journals. From a total of 1713 articles, 139 articles were identified as being focused on the use of technology for assessment. The analytic tool guided the rigorous exploration of the literature regarding the types of technology being used, the educational goal, the type of assessment, and the degree of “transformation” afforded by the technology. Results showed that, in the sample investigated, TEA is used most frequently for formative peer learning, as part of the task design and feedback stages of the assessment cycle, and that social media has been a major affordance for this. Results are discussed with a view to fostering a future culture of inquiry and scholarship around TEA in higher education

A Study in Violinist Identification using Short-term Note Features

The perception of music expression and emotion are greatly influenced by performer's individual interpretation, thus modelling performer's style is important to music understanding, style transfer, music education and characteristic music generation. This Thesis proposes approaches for modelling and identifying musical instrumentalists, using violinist identification as a case study. In violin performance, vibrato and timbre play important roles in players’ emotional expression, and they are key factors of playing style while execution shows great diversity. To validate that these two factors are effective to model violinists, we design and extract note-level vibrato features and timbre features from isolated concerto music notes, then present a violinist identification method based on the similarity of feature distributions, using single feature as well as fused features. The result shows that vibrato features are helpful for the violinist identification, and some timbre features perform better than vibrato features. In addition, the accuracy obtained from fused features is higher than using any single feature. However, apart from performer, the timbre is also determined by musical instruments, recording conditions and other factors. Furthermore, the common scenario for violinist identification is based on short music clips rather than isolated notes. To solve these two problems, we further examine the method using note-level timbre features to recognize violinists from segmented solo music clips, then use it to identify master players from concerto fragments. The results show that the designed features and method work very well for both types of music. Another experiment is conducted to examine the influence of instrument on the features. Results suggest that the selected timbre features can model performers’ individual playing reasonably and objectively, regardless of the instrument they play. Expressive timing is another key factor to reflect individual play styles. This Thesis develops a novel onset time deviation feature, which is used to model and identify master violinists on concerto fragments data. Results show that it performs better than timbre features on the dataset. To generalise the violinist identification method and further improve the result, deep learning methods are proposed and investigated. We present a transfer learning approach for violinist identification from pre-trained music auto-tagging neural networks and singer identification models. We then transfer pre-trained weights and fine-tune the models using violin datasets and finally obtain violinist identification results. We compare our system with state-of-the-art works, which shows that our model outperforms them using our two datasets

Proceedings of the 2015 WA Chapter of MSA Symposium on Music Performance and Analysis

This publication, entitled Proceedings of the 2015 WA Chapter MSA Symposium on Music Performance and Analysis, is a double-blind peer-reviewed conference proceedings published by the Western Australian Chapter of the Musicological Society of Australia, in conjunction with the Western Australian Academy of Performing Arts, Edith Cowan University, edited by Jonathan Paget, Victoria Rogers, and Nicholas Bannan. The original symposium was held at the University of Western Australia, School of Music, on 12 December 2015. With the advent of performer-scholars within Australian Universities, the intersections between analytical knowledge and performance are constantly being re-evaluated and reinvented. This collection of papers presents several strands of analytical discourse, including: (1) the analysis of music recordings, particularly in terms of historical performance practices; (2) reinventions of the \u27page-to-stage\u27 paradigm, employing new analytical methods; (3) analytical knowledge applied to pedagogy, particularly concerning improvisation; and (4) so-called \u27practice-led\u27 research.https://ro.ecu.edu.au/ecubooks/1005/thumbnail.jp

The acoustics of the violin: a review.

To understand the design and function of the violin requires investigation of a range of scientific questions. This paper presents a review: the relevant physics covers the nonlinear vibration of a bowed string, the vibration of the instrument body, and the consequent sound radiation. Questions of discrimination and preference by listeners and players require additional studies using the techniques of experimental psychology, and these are also touched on in the paper. To address the concerns of players and makers of instruments requires study of the interaction of all these factors, coming together in the concept of 'playability' of an instrument.This is the author accepted manuscript. The final version is available from IOP Science at http://iopscience.iop.org/0034-4885/77/11/115901

Discriminating music performers by timbre: On the relation between instrumental gesture, tone quality and perception in classical cello performance

Classical music performers use instruments to transform the symbolic notationof the score into sound which is ultimately perceived by a listener. For acoustic instruments, the timbre of the resulting sound is assumed to be strongly linked to the physical and acoustical properties of the instrument itself. However, rather little is known about how much influence the player has over the timbre of the sound — is it possible to discriminate music performers by timbre? This thesis explores player-dependent aspects of timbre, serving as an individual means of musical expression. With a research scope narrowed to analysis of solo cello recordings, the differences in tone quality of six performers who played the same musical excerpts on the same cello are investigated from three different perspectives: perceptual, acoustical and gestural. In order to understand how the physical actions that a performer exerts on an instrument affect spectro-temporal features of the sound produced, which then can be perceived as the player’s unique tone quality, a series of experiments are conducted, starting with the creation of dedicated multi-modal cello recordings extended by performance gesture information (bowing control parameters). In the first study, selected tone samples of six cellists are perceptually evaluated across various musical contexts via timbre dissimilarity and verbal attribute ratings. The spectro-temporal analysis follows in the second experiment, with the aim to identify acoustic features which best describe varying timbral characteristics of the players. Finally, in the third study, individual combinationsof bowing controls are examined in search for bowing patterns which might characterise each cellist regardless of the music being performed. The results show that the different players can be discriminated perceptually, by timbre, and that this perceptual discrimination can be projected back through the acoustical and gestural domains. By extending current understanding of human-instrument dependencies for qualitative tone production, this research may have further applications in computer-aided musical training and performer-informed instrumental sound synthesis.This work was supported by a UK EPSRC DTA studentship EP/P505054/1 and the EPSRC funded OMRAS2 project EP/E017614/1

Where's the transformation? Unlocking the potential of technology-enhanced assessment

This study provides insight into technology-enhanced assessment (TEA) in diverse higher education contexts. The effectiveness of using technology for assessment in higher education is still equivocal, particularly in regard to evidence of improvements in student learning. This empirical research explores the affordances that technology offers to assessment for transforming student learning. A systematic literature review, guided by an analytic survey tool, was used to identify and interrogate recent scholarly articles published in 19 international journals. From a total of 1713 articles, 139 articles were identified as being focused on the use of technology for assessment. The analytic tool guided the rigorous exploration of the literature regarding the types of technology being used, the educational goal, the type of assessment, and the degree of “transformation” afforded by the technology. Results showed that, in the sample investigated, TEA is used most frequently for formative peer learning, as part of the task design and feedback stages of the assessment cycle, and that social media has been a major affordance for this. Results are discussed with a view to fostering a future culture of inquiry and scholarship around TEA in higher education

Multisensory learning in adaptive interactive systems

The main purpose of my work is to investigate multisensory perceptual learning and sensory integration in the design and development of adaptive user interfaces for educational purposes. To this aim, starting from renewed understanding from neuroscience and cognitive science on multisensory perceptual learning and sensory integration, I developed a theoretical computational model for designing multimodal learning technologies that take into account these results. Main theoretical foundations of my research are multisensory perceptual learning theories and the research on sensory processing and integration, embodied cognition theories, computational models of non-verbal and emotion communication in full-body movement, and human-computer interaction models. Finally, a computational model was applied in two case studies, based on two EU ICT-H2020 Projects, "weDRAW" and "TELMI", on which I worked during the PhD

Measuring Expressive Music Performances: a Performance Science Model using Symbolic Approximation

Music Performance Science (MPS), sometimes termed systematic musicology in Northern Europe, is concerned with designing, testing and applying quantitative measurements to music performances. It has applications in art musics, jazz and other genres. It is least concerned with aesthetic judgements or with ontological considerations of artworks that stand alone from their instantiations in performances. Musicians deliver expressive performances by manipulating multiple, simultaneous variables including, but not limited to: tempo, acceleration and deceleration, dynamics, rates of change of dynamic levels, intonation and articulation. There are significant complexities when handling multivariate music datasets of significant scale. A critical issue in analyzing any types of large datasets is the likelihood of detecting meaningless relationships the more dimensions are included. One possible choice is to create algorithms that address both volume and complexity. Another, and the approach chosen here, is to apply techniques that reduce both the dimensionality and numerosity of the music datasets while assuring the statistical significance of results. This dissertation describes a flexible computational model, based on symbolic approximation of timeseries, that can extract time-related characteristics of music performances to generate performance fingerprints (dissimilarities from an ‘average performance’) to be used for comparative purposes. The model is applied to recordings of Arnold Schoenberg’s Phantasy for Violin with Piano Accompaniment, Opus 47 (1949), having initially been validated on Chopin Mazurkas.1 The results are subsequently used to test hypotheses about evolution in performance styles of the Phantasy since its composition. It is hoped that further research will examine other works and types of music in order to improve this model and make it useful to other music researchers. In addition to its benefits for performance analysis, it is suggested that the model has clear applications at least in music fraud detection, Music Information Retrieval (MIR) and in pedagogical applications for music education

Modelling Instrumental Gestures and Techniques: A Case Study of Piano Pedalling

PhD ThesisIn this thesis we propose a bottom-up approach for modelling instrumental gestures and techniques, using piano pedalling as a case study. Pedalling gestures play a vital role in expressive piano performance. They can be categorised into di erent pedalling techniques. We propose several methods for the indirect acquisition of sustain-pedal techniques using audio signal analyses, complemented by the direct measurement of gestures with sensors. A novel measurement system is rst developed to synchronously collect pedalling gestures and piano sound. Recognition of pedalling techniques starts by using the gesture data. This yields high accuracy and facilitates the construction of a ground truth dataset for evaluating the audio-based pedalling detection algorithms. Studies in the audio domain rely on the knowledge of piano acoustics and physics. New audio features are designed through the analysis of isolated notes with di erent pedal e ects. The features associated with a measure of sympathetic resonance are used together with a machine learning classi er to detect the presence of legato-pedal onset in the recordings from a speci c piano. To generalise the detection, deep learning methods are proposed and investigated. Deep Neural Networks are trained using a large synthesised dataset obtained through a physical-modelling synthesiser for feature learning. Trained models serve as feature extractors for frame-wise sustain-pedal detection from acoustic piano recordings in a proposed transfer learning framework. Overall, this thesis demonstrates that recognising sustain-pedal techniques is possible to a high degree of accuracy using sensors and also from audio recordings alone. As the rst study that undertakes pedalling technique detection in real-world piano performance, it complements piano transcription methods. Moreover, the underlying relations between pedalling gestures, piano acoustics and audio features are identi ed. The varying e ectiveness of the presented features and models can also be explained by di erences in pedal use between composers and musical eras

Evaluating Embodiment in Musical Instrument Modification and Augmentation

This PhD seeks to identify key aspects that optimise the learning process of new musical interfaces by professional musicians. Modifying or extending an existing musical instrument can impact players' skills. Fluency of execution or pitch accuracy can deteriorate due to demands on the performer's attention from the unfamiliarity of the instrument. As a result, players may require additional training on a modified instrument before they regain their fluency. The problem is that performers, especially professional players who have already invested many years in the unmodified musical instrument, might prefer to start from a high level. Thus, designing a new instrument that builds upon existing skills can be appealing. However, which design strategies might support such a goal? Which aspects of the original design should be preserved? How can we assess whether the resulting modified instrument allows the performer to retain their skills? This research presents four studies that tackle these questions. Results from the first two studies suggest that the design strategy should focus on participants’ sensorimotor imagery rather than the instrument's auditory feedback. During these studies, participants were still able to retain their fluency and pitch accuracy even in the presence of disrupting or irrelevant auditory feedback. Two additional studies propose quantitative methods to evaluate skill retention in instrument modification. This research can advise designers on whether they are on the right track in crafting an interface that builds upon existing skills. This challenge may apply to augmented instruments, the modification of existing musical instruments, or new digital instruments