Yo Tomita

Towards an intelligent score following system: handling of mistakes and jumps encountered during piano practicing

Score following has been an important area of research in AI and music since the mid 80s. Various systems were developed, but they were predominantly for providing automated accompaniment to live concert performances, dealing mostly with issues relating to pitch detection and identification of embellished melodies. They have a big potential in the area of education where student performers benefit in practice situations. Current accompaniment systems are not designed to deal with errors that may occur during practising. In this paper we present a system developed to provide accompaniment for students practising at home. First a survey of score following will be given. Then the capabilities of the system will be explained, and the results from the first experiments of the monophonic score following system will be presented.

Writer Identification in Old Music Manuscripts Using Contour-Hinge Feature and Dimensionality Reduction with an Autoencoder.

Although most of the previous studies in writer identification in music scores assumed successful prior staff-line removal, this assumption does not hold when the music scores suffer from a certain level of degradation or deformation. The impact of staff-line removal on the result of writer identification in such documents is rather vague. In this study, we propose a novel writer identification method that requires no staff-line removal and no segmentation. Staff-line removal is virtually achieved without image processing, by dimensionality reduction with an autoencoder in Contour-Hinge feature space. The experimental result with a wide range of music manuscripts shows the proposed method can achieve favourable results without prior staff-line removal.

Musicologist-driven writer identification in early music manuscripts

Recent renewed interest in computational writer identification has resulted in an increased number of publications. In relation to historical musicology its application has so far been limited. One of the obstacles seems to be that the clarity of the images from the scans available for computational analysis is often not sufficient. In this paper, the use of the Hinge feature is proposed to avoid segmentation and staff-line removal for effective feature extraction from low quality scans. The use of an auto encoder in Hinge feature space is suggested as an alternative to staff-line removal by image processing, and their performance is compared. The result of the experiment shows an accuracy of 87 % for the dataset containing 84 writers’ samples, and superiority of our segmentation and staff-line removal free approach. Practical analysis on Bach’s autograph manuscript of the Well-Tempered Clavier II (Additional MS. 35021 in the British Library, London) is also presented and the extensive applicability of our approach is demonstrated.

New technology and piano study in higher education: getting the most out of Computer-Controlled Player Pianos

So far, the ever-increasing popularity of music technology has had little effect on the way we conduct performance studies. However, with the appearance of Computer Controlled Player Pianos, such as the Yamaha Disklavier and Bosendorfer SE, the technology is just waiting to be used. In this article, the authors examine various ways in which this technology can be used to enhance traditional methods of piano teaching.