Beating-time gestures imitation learning for humanoid robots

Beating-time gestures are movement patterns of the hand swaying along with music, thereby indicating accented musical pulses. The spatiotemporal configuration of these patterns makes it diÿcult to analyse and model them. In this paper we present an innovative modelling approach that is based upon imitation learning or Programming by Demonstration (PbD). Our approach - based on Dirichlet Process Mixture Models, Hidden Markov Models, Dynamic Time Warping, and non-uniform cubic spline regression - is particularly innovative as it handles spatial and temporal variability by the generation of a generalised trajectory from a set of periodically repeated movements. Although not within the scope of our study, our procedures may be implemented for the sake of controlling movement behaviour of robots and avatar animations in response to music

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

STUDY OF HAND GESTURE RECOGNITION AND CLASSIFICATION

To recognize different hand gestures and achieve efficient classification to understand static and dynamic hand movements used for communications.Static and dynamic hand movements are first captured using gesture recognition devices including Kinect device, hand movement sensors, connecting electrodes, and accelerometers. These gestures are processed using hand gesture recognition algorithms such as multivariate fuzzy decision tree, hidden Markov models (HMM), dynamic time warping framework, latent regression forest, support vector machine, and surface electromyogram. Hand movements made by both single and double hands are captured by gesture capture devices with proper illumination conditions. These captured gestures are processed for occlusions and fingers close interactions for identification of right gesture and to classify the gesture and ignore the intermittent gestures. Real-time hand gestures recognition needs robust algorithms like HMM to detect only the intended gesture. Classified gestures are then compared for the effectiveness with training and tested standard datasets like sign language alphabets and KTH datasets. Hand gesture recognition plays a very important role in some of the applications such as sign language recognition, robotics, television control, rehabilitation, and music orchestration

Sharing musical expression through embodied listening: a case study based on Chinese guqin music

In this study we report on the result of an experiment in which a guqin music performance was recorded and individual listeners were asked to move their arm along with the music that they heard. Movement velocity patterns were extracted from both the musician and the listeners. The analysis reveals that the listeners’ movement velocity patterns tend to correlate with each other, and with the movement velocity patterns of the player’s shoulders.The findings support the hypothesis that listeners and player share, to a certain degree, a sensitivity for musical expression and its associated corporeal intentionality

Capture, modeling and recognition of expert technical gestures in wheel-throwing art of pottery

International audienceThis research has been conducted in the context of the ArtiMuse project that aims at the modeling and renewal of rare gestural knowledge and skills involved in the traditional craftsmanship and more precisely in the art of the wheel-throwing pottery. These knowledge and skills constitute the Intangible Cultural Heritage and refer to the fruit of diverse expertise founded and propagated over the centuries thanks to the ingeniousness of the gesture and the creativity of the human spirit. Nowadays, this expertise is very often threatened with disappearance because of the difficulty to resist to globalization and the fact that most of those "expertise holders" are not easily accessible due to geographical or other constraints. In this paper, a methodological framework for capturing and modeling gestural knowledge and skills in wheel-throwing pottery is proposed. It is based on capturing gestures using wireless inertial sensors and statistical modeling. In particular, we used a system that allows for online alignment of gestures using a modified Hidden Markov Model. This methodology is implemented into a Human-Computer Interface, which permits both the modeling and recognition of expert technical gestures. This system could be used to assist in the learning of these gestures by giving continuous feedback in real-time by measuring the difference between expert and learner gestures. The system has been tested and evaluated on different potters with a rare expertise, which is strongly related to their local identity

Human-Computer Music Performance: From Synchronized Accompaniment to Musical Partner

Live music performance with computers has motivated many research projects in science, engineering, and the arts. In spite of decades of work, it is surprising that there is not more technology for, and a better understanding of the computer as music performer. We review the development of techniques for live music performance and outline our efforts to establish a new direction, Human-Computer Music Performance (HCMP), as a framework for a variety of coordinated studies. Our work in this area spans performance analysis, synchronization techniques, and interactive performance systems. Our goal is to enable musicians to ncorporate computers into performances easily and effectively through a better understanding of requirements, new techniques, and practical, performance-worthy implementations. We conclude with directions for future work

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

An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods