Masahiro Niitsuma

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.

Development of a communication system using audio signal using audible frequency bands

This paper proposes a technique of embedding information into audio signal to communicate using loudspeakers and microphones mounted on tablet-type devices. This technique is based on amplitude modulation for high audible frequency bands. The embedded information is represented by existence of sine waves with 11 different frequencies. In consideration of the frequency characteristics of tablet-type devices, two frequency bands to embed information, were chosen: (1) frequencies up to 12 kHz; (2) frequencies up to 16 kHz. The experimental result showed 85.9% of correct decoding in the case of (1), and 76.8% in the case of (2). Moreover, less audible noise was perceived in the case of (2).

Automatic recognition of negative emotion in speech using support vector machine

This paper addresses negative emotion recognition using paralinguistic information in speech for speech dialogue system. Speech conveys not only linguistic information but also paralinguistic and non-linguistic information such as the emotions, attitudes, and intentions. This easily perceivable information plays a key role in a spoken dialog system. However, most of previous speech recognition systems fail to consider this significant information, focusing only on linguistic information, thus hindering the development of more natural speech dialog systems. In order to utilize these significant information for speech dialog systems, this paper focuses on negative emotion recognition from Japanese utterances. 6552-dimensional acoustic features were extracted from 6300 Japanese utterances of 50 people in three emotional state: negative; positive; and neutral. Negative emotion includes anger, sad and dislike. While positive emotion includes favor, joy, and relief. They were classified by SVM and evaluated by ...

Age estimation in Japanese speech based on feature selection

This paper addresses age estimation of Japanese speech utterance, using paralinguistic information in speech. Our speech conveys not only linguistic information but also paralinguistic and non-linguistic information. Nonlinguistic information includes potentially valuable social information such as personal parameters, body conditions, gender and ages. This significant information, however, has not been investigated enough. The main purpose of our study was to develop less-invasive methods to extract this information, by proposing a method to classify speakers’ age into five groups (20s, 30s, 40s, 50s, and 60s). 6375-dimentional audio features were extracted from 1579 samples of 913 male speakers extracted from CSJ (Corpus of Spontaneous Japanese) and feature selection was conducted based on Fishers Ratio. Naive Bayes with top 20 parameters of Fischer’s ratio, yields highest accuracy rate of 51.2%.