A New Medium for Remote Music Tuition

It is common to learn to play an orchestral musical instrument through regular one-to-one lessons with an experienced musician as a tutor. Students may work with the same tutor for many years, meeting regularly to receive real-time, iterative feedback on their performance. However, musicians travel regularly to audition, teach and perform and this can sometimes make it difficult to maintain regular contact. In addition, an experienced tutor for a specific instrument or musical style may not be available locally. General instrumental tuition may not be available at all in geographically distributed communities. One solution is to use technology such as videoconference to facilitate a remote lesson; however, this fundamentally changes the teaching interaction. For example, as a result of the change in communication medium, the availability of non-verbal cues and perception of relative spatiality is reduced. We describe a study using video-ethnography, qualitative video analysis and conversation analysis to make a fine-grained examination of student–tutor interaction during five co-present and one video-mediated woodwind lesson. Our findings are used to propose an alternative technological solution – an interactive digital score. Rather than the face-to-face configuration enforced by videoconference, interacting through a shared digital score, augmented by visual representation of the social cues found to be commonly used in co-present lessons, will better support naturalistic student–tutor interaction during the remote lesson experience. Our findings may also be applicable to other fields where knowledge and practice of a physical skill sometimes need to be taught remotely, such as surgery or dentistry

Understanding Optical Music Recognition

For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR). However, this field is still difficult to access for new researchers, especially those without a significant musical background: Few introductory materials are available, and, furthermore, the field has struggled with defining itself and building a shared terminology. In this work, we address these shortcomings by (1) providing a robust definition of OMR and its relationship to related fields, (2) analyzing how OMR inverts the music encoding process to recover the musical notation and the musical semantics from documents, and (3) proposing a taxonomy of OMR, with most notably a novel taxonomy of applications. Additionally, we discuss how deep learning affects modern OMR research, as opposed to the traditional pipeline. Based on this work, the reader should be able to attain a basic understanding of OMR: its objectives, its inherent structure, its relationship to other fields, the state of the art, and the research opportunities it affords

Exploring the Features to Classify the Musical Period of Western Classical Music

Music Information Retrieval (MIR) focuses on extracting meaningful information from music content. MIR is a growing field of research with many applications such as music recommendation systems, fingerprinting, query-by-humming or music genre classification. This study aims to classify the styles of Western classical music, as this has not been explored to a great extent by MIR. In particular, this research will evaluate the impact of different music characteristics on identifying the musical period of Baroque, Classical, Romantic and Modern. In order to easily extract features related to music theory, symbolic representation or music scores were used, instead of audio format. A collection of 870 Western classical music piano scores was downloaded from different sources such as KernScore library (humdrum format) or the Musescore community (MusicXML format). Several global features were constructed by parsing the files and accessing the symbolic information, including notes and duration. These features include melodic intervals, chord types, pitch and rhythm histograms and were based on previous studies and music theory research. Using a radial kernel support vector machine algorithm, different classification models were created to analyse the contribution of the main musical properties: rhythm, pitch, harmony and melody. The study findings revealed that the harmony features were significant predictors of the music styles. The research also confirmed that the musical styles evolved gradually and that the changes in the tonal system through the years, appeared to be the most significant change to identify the styles. This is consistent with the findings of other researchers. The overall accuracy of the model using all the available features achieved an accuracy of 84.3%. It was found that of the four periods studied, it was most difficult to classify music from the Modern period