If a device can control human hands, the device can be useful for HCI and tangible application's output. To aid the controlling of finger movement, we present PossessedHand, a device with a forearm belt that can inform when and which fingers should be moved. PossessedHand controls the user's fingers by applying electrical stimulus to the muscles around the forearm. Each muscle is stimulated via 28 electrode pads. Muscles at different depths in the forearm can be selected for simulation by varying the stimulation level. PossessedHand can automatically calibrate the system for individuals. The automatic calibration system estimates relations between each electrode pad, stimulation level and muscle movement. Experiments show that PossessedHand can control the motion of 16 joints in the hand. Further, we also discuss an application based on this device to aid in playing a musical instrument.

PossessedHand: techniques for controlling human hands using electrical muscles stimuli

Fundamentals of Music Processing

Human and Machine Hearing is the first book to comprehensively describe how human hearing works and how to build machines to analyze sounds in the same way that people do. Drawing on over thirty-five years of experience in analyzing hearing and building systems, Richard F. Lyon explains how we can now build machines with close-to-human abilities in speech, music, and other sound-understanding domains. He explains human hearing in terms of engineering concepts, and describes how to incorporate those concepts into machines for a wide range of modern applications. The details of this approach are presented at an accessible level, to bring a diverse range of readers, from neuroscience to engineering, to a common technical understanding. The description of hearing as signal-processing algorithms is supported by corresponding open-source code, for which the book serves as motivating documentation.

Human and Machine Hearing: Extracting Meaning from Sound

This paper presents a beat tracking system that processes musical acoustic signals and recognizes temporal positions of beats in real time. Most previous systems were not able to deal with audio signals that contained sounds of various instruments, especially drums. Our system deals with popular music in which drums maintain the beat. To examine multiple hypotheses of beats in parallel, our system manages multiple agents that predict beats by using autocorrelation and cross-correlation according to different strategies. In our experiment with eight pre-registered drum patterns, the system implemented on a parallel computer correctly tracked beats in 42 out of 44 commercially distributed popular songs.

A Real - time Beat Tracking System for Audio Signals - Beat Tracking for Music with out Drum - sounds-

UI (AUI) 316 activity, structure of 258 Activity Theory (AT) 249, 250 Activity Theory, as model for investigation and analysis 256 Advancing Clinico-Genomic Trials on Cancer (ACGT) 341 aggregation (community), influence of level of 196 annotate score 294 appreciative inquiry (AI) 61, 62 appreciative model, for knowledge sharing 70 asynchronous activities, realization of 393 asynchronous tasks 427 audience communities 381 augmented music toolkit 283 avatar 192 B Benkler’s design typology 90 BioMOBY 222 Blogs 209 Bridging Research and Practice (BRAP) 276 Business Process Model Notation (BPMN) 311

Virtual Community Practices and Social Interactive Media: Technology Lifecycle and Workflow Analysis

This paper presents a survey of recent work in computer-assisted musical instrumental tutoring and outlines several questions to consider when developing future projects. In particular, we suggest that the area ingreatest need of computer assistance is enhancing daily practice: both motivating students to practice through games and multimedia, and providing an objective analysis of the students' performance. Many existing projects attempt to replace human teachers by providing lessons during daily practice; in most cases, these "daily lessons" are not necessary.

/pdf/effective-use-of-multimedia-for-computer-assisted-musical-hzqvn9gi5f.pdf

Effective use of multimedia for computer-assisted musical instrument tutoring

Algorithms for musical tempo estimation have become increasingly complicated in recent years. These algorithms typically utilize two fundamental properties of musical rhythm: some features of the audio signal are self-similar at periods related to the underlying rhythmic structure, and rhythmic events tend to be spaced regularly in time. We present a streamlined tempo estimation method (stem) that distills ideas from previous work by reducing the number of steps, parameters, and modeling assumptions while retaining good accuracy. This method is designed for music with a constant or near-constant tempo. The proposed method either outperforms or has similar performance to many existing state-of-the-art algorithms. Self-similarity is captured through autocorrelation of the onset strength signal (OSS), and time regularity is captured through cross-correlation of the OSS with regularly spaced pulses. Our findings are supported by the most comprehensive evaluation of tempo estimation algorithms to date in terms of the number of datasets and tracks considered. During the process we have also corrected ground truth annotations for the datasets considered. All the data, the annotations, the evaluation code, and three different implementations (C++, Python, MATLAB) of the proposed algorithm are provided in order to support reproducibility.

/pdf/streamlined-tempo-estimation-based-on-autocorrelation-and-gvo59j5hws.pdf

Streamlined tempo estimation based on autocorrelation and cross-correlation with pulses

Composition, listening, and performance are essential activities in classroom music education, yet conventional music classes impose unnecessary limitations on students' ability to develop these skills. Based on in-depth fieldwork and a user-centered design approach, we created MOGCLASS, a multimodal collaborative music environment that enhances students' musical experience and improves teachers' management of the classroom. We conducted a two-round system evaluation to improve the prototype and evaluate the system: Improvements were made based on the results from an iterative design evaluation, in which a trial system was implemented. The system then underwent a second round of evaluation through a three-week between-subject controlled experiment in a local primary school. Results showed that MOGCLASS is effective in motivating students to learn music, improving the way they collaborate with other students as well as helping teachers manage the classroom.

/pdf/mogclass-evaluation-of-a-collaborative-system-of-mobile-2wgf9yy8sv.pdf

MOGCLASS: evaluation of a collaborative system of mobile devices for classroom music education of young children

Tempo estimation is a fundamental problem in music information retrieval. It also forms the basis of other types of rhythmic analysis such as beat tracking and pattern detection. There is a large body of work in tempo estimation using a variety of different approaches that differ in their accuracy as well as their complexity. Fundamentally they take advantage of two properties of musical rhythm: 1) the music signal tends to be self-similar at periodicities related to the underlying rhythmic structure, 2) rhythmic events tend to be spaced regularly in time. We propose an algorithm for tempo estimation that is based on these two properties. We have tried to reduce the number of steps, parameters and modeling assumptions while retaining good performance and causality. The proposed approach outperforms a large number of existing tempo estimation methods and has similar performance to the best-performing ones. We believe that we have conducted the most comprehensive evaluation to date of tempo induction algorithms in terms of number of datasets and tracks.

/pdf/an-effective-simple-tempo-estimation-method-based-on-self-hvdsbobq5a.pdf

An effective, simple tempo estimation method based on self-similarity and regularity

We present a software tool which analyses the technical ability of saxophonists. In an academic practicing context, control of the air pressure is an important aspect which is strongly correlated to the academic level of the performer. We propose several methods for evaluating the performer by considering the evolution of the fundamental frequency during the performance of specially designed exercises. We show how those metrics can be computed in real time and discuss the pedagogical feedback ability of the proposed tool.

/pdf/analysis-of-saxophone-performance-for-computer-assisted-4uligsz1jq.pdf

Graham Percival

Papers

Effective use of multimedia for computer-assisted musical instrument tutoring

Streamlined tempo estimation based on autocorrelation and cross-correlation with pulses

MOGCLASS: evaluation of a collaborative system of mobile devices for classroom music education of young children

An effective, simple tempo estimation method based on self-similarity and regularity

Analysis of saxophone performance for computer-assisted tutoring