Machine Learning of Personal Gesture Variation in Music Conducting

This note presents a system that learns expressive and id- iosyncratic gesture variations for gesture-based interaction. The system is used as an interaction technique in a music con- ducting scenario where gesture variations drive music articu- lation. A simple model based on Gaussian Mixture Modeling is used to allow the user to configure the system by provid- ing variation examples. The system performance and the in- fluence of user musical expertise is evaluated in a user study, which shows that the model is able to learn idiosyncratic vari- ations that allow users to control articulation, with better per- formance for users with musical expertise

Designing and evaluating the usability of a machine learning API for rapid prototyping music technology

To better support creative software developers and music technologists' needs, and to empower them as machine learning users and innovators, the usability of and developer experience with machine learning tools must be considered and better understood. We review background research on the design and evaluation of application programming interfaces (APIs), with a focus on the domain of machine learning for music technology software development. We present the design rationale for the RAPID-MIX API, an easy-to-use API for rapid prototyping with interactive machine learning, and a usability evaluation study with software developers of music technology. A cognitive dimensions questionnaire was designed and delivered to a group of 12 participants who used the RAPID-MIX API in their software projects, including people who developed systems for personal use and professionals developing software products for music and creative technology companies. The results from the questionnaire indicate that participants found the RAPID-MIX API a machine learning API which is easy to learn and use, fun, and good for rapid prototyping with interactive machine learning. Based on these findings, we present an analysis and characterization of the RAPID-MIX API based on the cognitive dimensions framework, and discuss its design trade-offs and usability issues. We use these insights and our design experience to provide design recommendations for ML APIs for rapid prototyping of music technology. We conclude with a summary of the main insights, a discussion of the merits and challenges of the application of the CDs framework to the evaluation of machine learning APIs, and directions to future work which our research deems valuable

Music conducting pedagogy and technology : a document analysis on best practices

This document analysis was designed to investigate pedagogical practices of music conducting teachers in conjunction with research of technologists on the use of various technologies as teaching tools. I sought to discern how conducting teachers and pedagogues are applying recent technological advancements into their teaching strategies. I also sought to understand what paths research is taking about the use of software, hardware, and computer systems applied to the teaching of music conducting technique. This dissertation was guided by four main research questions: (1) How has technology been used to aid in the teaching of conducting? (2) What is the role of technology in the context of conducting pedagogy? (3) Given that conducting is a performative act, how can it be developed through technological means? (4) What technological possibilities exist in the teaching of music conducting technique? Data were collected through music conducting syllabi, conducting textbooks, and research articles. Documents were selected through purposive sampling procedures. Analysis of documents through the constant comparative approach identified emerging themes and differences across the three types of documents. Based on a synthesis of information, I discussed implications for conducting pedagogy and made suggestions for conducting educators.Includes bibliographical references

Dalcroze meets technology : integrating music, movement and visuals with the Music Paint Machine

peer reviewedNew interactive music educational technologies are often seen as a ‘force of change’, introducing new approaches that address the shortcomings (e.g. score-based, teacher-centred and disembodied) of the so-called traditional teaching approaches. And yet, despite the growing belief in their educational potential, these new technologies have been problematised with regard to their design, reception, implementation and evaluation. A possible way to optimise the realisation of the educational potential of interactive music educational technologies is to connect their use to music educational approaches that stood the test of time and as such may inspire technologies to become a bridge between tradition and innovation. This article describes an educational technology (the Music Paint Machine) that integrates the creative use of movement and visualisation to support instrumental music teaching and learning. Next, it connects this application to such an established music educational method, the Dalcroze approach. Through the lens of a set of interconnected aspects, it is shown how the Music Paint Machine’s conceptual design aligns to the underlying principles of this approach. In this way, it is argued that integrating Dalcroze-inspired practices is a plausible way of realising the didactic potential of the system. An appendix with example exercises is provided

Robust correlated and individual component analysis

© 1979-2012 IEEE.Recovering correlated and individual components of two, possibly temporally misaligned, sets of data is a fundamental task in disciplines such as image, vision, and behavior computing, with application to problems such as multi-modal fusion (via correlated components), predictive analysis, and clustering (via the individual ones). Here, we study the extraction of correlated and individual components under real-world conditions, namely i) the presence of gross non-Gaussian noise and ii) temporally misaligned data. In this light, we propose a method for the Robust Correlated and Individual Component Analysis (RCICA) of two sets of data in the presence of gross, sparse errors. We furthermore extend RCICA in order to handle temporal incongruities arising in the data. To this end, two suitable optimization problems are solved. The generality of the proposed methods is demonstrated by applying them onto 4 applications, namely i) heterogeneous face recognition, ii) multi-modal feature fusion for human behavior analysis (i.e., audio-visual prediction of interest and conflict), iii) face clustering, and iv) thetemporal alignment of facial expressions. Experimental results on 2 synthetic and 7 real world datasets indicate the robustness and effectiveness of the proposed methodson these application domains, outperforming other state-of-the-art methods in the field

Inclusive improvisation: exploring the line between listening and playing music

The field of Accessible Digital Musical Instruments (ADMIs) is growing rapidly, with instrument designers recognising that adaptations to existing Digital Musical Instruments (DMIs) can foster inclusive music making. ADMIs offer opportunities to engage with a wider range of sounds than acoustic instruments. Furthermore, gestural ADMIs free the music maker from relying on screen, keyboard, and mouse-based interfaces for engaging with these sounds. This brings greater opportunities for exploration, improvisation, empowerment, and flow through music making for people with disability and the communities of practice they are part of. This article argues that developing ADMIs from existing DMIs can speed up the process and allow for more immediate access for those with diverse needs. It presents three case studies of a gestural DMI, originally designed by the first author for his own creative practice, played by people with disability in diverse contexts. The article shows that system-based considerations that enabled an expert percussionist to achieve virtuoso performances with the instrument required minimal hardware and software changes to facilitate greater inclusivity. Understanding the needs of players and customising the system-based movement to sound mappings was of far greater importance in making the instrument accessible

Emerging technologies for learning (volume 1)

Collection of 5 articles on emerging technologies and trend

Tied factor analysis for face recognition across large pose differences

Face recognition algorithms perform very unreliably when the pose of the probe face is different from the gallery face: typical feature vectors vary more with pose than with identity. We propose a generative model that creates a one-to-many mapping from an idealized “identity” space to the observed data space. In identity space, the representation for each individual does not vary with pose. We model the measured feature vector as being generated by a pose-contingent linear transformation of the identity variable in the presence of Gaussian noise. We term this model “tied” factor analysis. The choice of linear transformation (factors) depends on the pose, but the loadings are constant (tied) for a given individual. We use the EM algorithm to estimate the linear transformations and the noise parameters from training data. We propose a probabilistic distance metric that allows a full posterior over possible matches to be established. We introduce a novel feature extraction process and investigate recognition performance by using the FERET, XM2VTS, and PIE databases. Recognition performance compares favorably with contemporary approaches