Interactive Music Systems

Children with special needs often find difficulty learning due to their disabilities. Music therapy is an alternate method of learning music composition that benefits those with special needs. This project developed a music technology application that addressed needs related to facilitating musicianship with special needs populations. The application was implemented in a public school classroom where professional educators surveyed its viability as an instructional tool. The data received from the survey suggest that this survey suggest that the application would be a viable instructional resource

Human Motion Analysis for Efficient Action Recognition

Automatic understanding of human actions is at the core of several application domains, such as content-based indexing, human-computer interaction, surveillance, and sports video analysis. The recent advances in digital platforms and the exponential growth of video and image data have brought an urgent quest for intelligent frameworks to automatically analyze human motion and predict their corresponding action based on visual data and sensor signals. This thesis presents a collection of methods that targets human action recognition using different action modalities. The first method uses the appearance modality and classifies human actions based on heterogeneous global- and local-based features of scene and humanbody appearances. The second method harnesses 2D and 3D articulated human poses and analyizes the body motion using a discriminative combination of the parts’ velocities, locations, and correlations histograms for action recognition. The third method presents an optimal scheme for combining the probabilistic predictions from different action modalities by solving a constrained quadratic optimization problem. In addition to the action classification task, we present a study that compares the utility of different pose variants in motion analysis for human action recognition. In particular, we compare the recognition performance when 2D and 3D poses are used. Finally, we demonstrate the efficiency of our pose-based method for action recognition in spotting and segmenting motion gestures in real time from a continuous stream of an input video for the recognition of the Italian sign gesture language

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

Interactive Tango Milonga: An Interactive Dance System for Argentine Tango Social Dance

abstract: When dancers are granted agency over music, as in interactive dance systems, the actors are most often concerned with the problem of creating a staged performance for an audience. However, as is reflected by the above quote, the practice of Argentine tango social dance is most concerned with participants internal experience and their relationship to the broader tango community. In this dissertation I explore creative approaches to enrich the sense of connection, that is, the experience of oneness with a partner and complete immersion in music and dance for Argentine tango dancers by providing agency over musical activities through the use of interactive technology. Specifically, I create an interactive dance system that allows tango dancers to affect and create music via their movements in the context of social dance. The motivations for this work are multifold: 1) to intensify embodied experience of the interplay between dance and music, individual and partner, couple and community, 2) to create shared experience of the conventions of tango dance, and 3) to innovate Argentine tango social dance practice for the purposes of education and increasing musicality in dancers.Dissertation/ThesisDoctoral Dissertation Music 201

Robot Learning from Human Demonstration: Interpretation, Adaptation, and Interaction

Robot Learning from Demonstration (LfD) is a research area that focuses on how robots can learn new skills by observing how people perform various activities. As humans, we have a remarkable ability to imitate other human’s behaviors and adapt to new situations. Endowing robots with these critical capabilities is a significant but very challenging problem considering the complexity and variation of human activities in highly dynamic environments. This research focuses on how robots can learn new skills by interpreting human activities, adapting the learned skills to new situations, and naturally interacting with humans. This dissertation begins with a discussion of challenges in each of these three problems. A new unified representation approach is introduced to enable robots to simultaneously interpret the high-level semantic meanings and generalize the low-level trajectories of a broad range of human activities. An adaptive framework based on feature space decomposition is then presented for robots to not only reproduce skills, but also autonomously and efficiently adjust the learned skills to new environments that are significantly different from demonstrations. To achieve natural Human Robot Interaction (HRI), this dissertation presents a Recurrent Neural Network based deep perceptual control approach, which is capable of integrating multi-modal perception sequences with actions for robots to interact with humans in long-term tasks. Overall, by combining the above approaches, an autonomous system is created for robots to acquire important skills that can be applied to human-centered applications. Finally, this dissertation concludes with a discussion of future directions that could accelerate the upcoming technological revolution of robot learning from human demonstration

Interfaces avanzados aplicados a la interacción musical

The latest advances in human-computer interaction technologies have brought forth changes in the way we interact with computing devices of any kind, from the standard desktop computer to the more recent smartphones. The development of these technologies has thus introduced new interaction metaphors that provide more enriching experiences for a wide range of different applications. Music is one of most ancient forms of art and entertainment that can be found in our legacy, and conforms a strong interactive experience on itself. The application of new technologies to enhance music computer-based interaction paradigms can potentially provide all sorts of improvements: providing low-cost access to music rehearsal, lowering knowledge barriers in regard to music learning, virtual instrument simulation, etc. Yet, surprisingly, there has been rather limited research on the application of new interaction models and technologies to the specific field of music interaction in regard to other areas. This thesis aims to address the aforementioned need by presenting a set of studies which cover the use of innovative interaction models for music-based applications, from interaction paradigms for music learning to more entertainment-oriented interaction interfaces, such as virtual musical instruments, ensemble conductor simulation, etc. The main contributions of this thesis are: · It is shown that the use of signal processing techniques on the music signal and music information retrieval techniques can create enticing interfaces for music learning. Concretely, the research conducted includes the implementation and experimental evaluation of a set of different learning-oriented applications which make use of these techniques to implement inexpensive, easy-to-use human-computer interfaces, which serve as support tools in music learning processes. · This thesis explores the use of tracking systems and machine learning techniques to achieve more sophisticated interfaces for innovative music interaction paradigms. Concretely, the studies conducted have shown that it is indeed feasible to emulate the functionally of musical instruments such as the drumkit or the theremin. In a similar way, it is shown that more complex musical roles can also be recreated through the use of new interaction models, such as the case of the ensemble conductor or a step-aerobics application. · The benefits in using advanced human-computer interfaces in musical experiences are review and assessed through experimental evaluation. It is shown that the addition of these interfaces contributes positively to user perception, providing more satisfying and enriching experiences overall. · The thesis also illustrates that the use of machine learning algoriths and signal processing along with new interaction devices provides an effective framework for human gesture recognition and prediction, and even mood estimation

Interaction Design for Digital Musical Instruments

The thesis aims to elucidate the process of designing interactive systems for musical performance that combine software and hardware in an intuitive and elegant fashion. The original contribution to knowledge consists of: (1) a critical assessment of recent trends in digital musical instrument design, (2) a descriptive model of interaction design for the digital musician and (3) a highly customisable multi-touch performance system that was designed in accordance with the model. Digital musical instruments are composed of a separate control interface and a sound generation system that exchange information. When designing the way in which a digital musical instrument responds to the actions of a performer, we are creating a layer of interactive behaviour that is abstracted from the physical controls. Often, the structure of this layer depends heavily upon: 1. The accepted design conventions of the hardware in use 2. Established musical systems, acoustic or digital 3. The physical configuration of the hardware devices and the grouping of controls that such configuration suggests This thesis proposes an alternate way to approach the design of digital musical instrument behaviour – examining the implicit characteristics of its composite devices. When we separate the conversational ability of a particular sensor type from its hardware body, we can look in a new way at the actual communication tools at the heart of the device. We can subsequently combine these separate pieces using a series of generic interaction strategies in order to create rich interactive experiences that are not immediately obvious or directly inspired by the physical properties of the hardware. This research ultimately aims to enhance and clarify the existing toolkit of interaction design for the digital musician