This study proposes a novel method for quantitatively and visually representing individual preferences for piano performance styles. Such preferences are often highly subjective and difficult to articulate, thus limiting effective learning and the development of personalized artistic expressions. Using features such as tempo, dynamics, and their first- and second-order derivatives, and leveraging the Approximate Inverse Model Explanation (AIME) framework from Explainable AI (XAI), the method analyzes preference labels assigned by learners to professional performances, enabling the visualization of integrated, preference-based performance styles independent of specific pieces. This approach allows learners to explicitly visualize and verbalize their preferences, incorporate them into their performances, and identify professional performers whose stylistic tendencies align with their preferences. The experimental results demonstrate that the proposed method effectively captures integrated stylistic tendencies across multiple pieces and reveals stylistic similarities among performers that may remain hidden when using conventional numerical metrics alone. By making the abstract concept of performance style preferences concrete, the proposed method provides a systematic, explainable, and personalized framework that lays the groundwork for future studies aimed at bridging the gap between subjective musical preferences and actual learning outcomes, with potential applications in individualized music education, artistic self-discovery, and enriching musical performance culture.

We introduced a novel approach to global sign language recognition by leveraging the capabilities of the Editable Mediator. Traditional methods have often been limited to recognizing sign languages from specific linguistic regions, necessitating ad hoc implementation for multilingual regions. Our method aims to bridge this gap by providing a unified framework for recognizing sign languages in various linguistic areas and promoting global communication. At the core of our system is the Editable Mediator, a mechanism that determines the actual sign meaning from various local hand-shape recognitions. Instead of focusing on specific sign language notations, such as HamNoSys, our approach emphasizes the recognition of common primitive actions shared across different sign languages. These primitive actions are recognized by multiple modules, and their combinations are interpreted by the Editable Mediator to determine the intended sign-language message. This architecture not only simplifies the recognition process, but also offers flexibility. By merely editing the Editable Mediator, our system can adapt to various sign languages worldwide without the need for extensive retraining or ad hoc implementation. This innovation reduces barriers to introducing new sign language systems and promotes a more inclusive global communication platform.

Through technology, it is essential to seamlessly bridge the divide between diverse speaking communities (including the signer (the sign language speaker) community). In order to realize communication that successfully conveys emotions, it is necessary to recognize not only verbal information but also non-verbal information. In the case of signers, there are two main types of behavior: verbal behavior and emotional behavior. This paper presents a sign language recognition method by similarity measure with emotional expression specific to signers. We focus on recognizing the sign language conveying verbal information itself and on recognizing emotional expression. Our method recognizes sign language by time-series similarity measure on a small amount of model data, and at the same time, recognizes emotion expression specific to signers. Our method extracts time-series features of the body, arms, and hands from sign language videos and recognizes them by measuring the similarity of the time-series features. In addition, it recognizes the emotional expressions specific to signers from the time-series features of their faces.