Understanding and identifying musical shape plays an important role in music
education and performance assessment. To simplify the otherwise time- and
cost-intensive musical shape evaluation, in this paper we explore how
artificial intelligence (AI) driven models can be applied. Considering musical
shape evaluation as a classification problem, a light-weight Siamese residual
neural network (S-ResNN) is proposed to automatically identify musical shapes.
To assess the proposed approach in the context of piano musical shape
evaluation, we have generated a new dataset, containing 4116 music pieces
derived by 147 piano preparatory exercises and performed in 28 categories of
musical shapes. The experimental results show that the S-ResNN significantly
outperforms a number of benchmark methods in terms of the precision, recall and
F1 score.

Expert Commentary: The Role of Artificial Intelligence in Musical Shape Evaluation

The article discusses the utilization of artificial intelligence (AI) driven models, specifically a light-weight Siamese residual neural network (S-ResNN), for automatic identification of musical shapes. This application of AI in the field of music education and performance assessment has the potential to simplify and enhance the process, which traditionally requires considerable time and cost.

The concept of musical shape refers to the patterns and structures within a piece of music that govern its overall form and structure. Understanding and identifying these musical shapes is important for musicians, as it aids in interpretation, expression, and communication of the music’s intended meaning to the listener.

The proposed S-ResNN model is designed to classify musical shapes by analyzing the features and patterns present in the input data. The model has been applied specifically to piano preparatory exercises, where 4116 music pieces were generated and categorized into 28 different musical shapes. The results obtained through experimentation demonstrate that the S-ResNN model outperforms several benchmark methods in terms of precision, recall, and F1 score, indicating its effectiveness in accurately identifying musical shapes.

The interdisciplinary nature of this research is evident, bringing together concepts from artificial intelligence, music education, and computer science. By utilizing AI techniques, insights from the field of multimedia information systems can be applied to enhance our understanding of music and its underlying structures.

Furthermore, this research aligns with the broader fields of animation, augmented reality, virtual reality, and artificial realities as it explores ways to automate the analysis and recognition of abstract elements in music. The implications of this work extend beyond traditional musical performance assessment, as it opens up possibilities for developing interactive multimedia experiences based on the analysis of musical shapes.

In conclusion, the application of artificial intelligence in musical shape evaluation holds immense potential for the field of music education and performance assessment. The use of the S-ResNN model, as proposed in this article, demonstrates promising results in automatically identifying musical shapes in piano preparatory exercises. This research contributes to the wider field of multimedia information systems and its relation to animations, artificial reality, augmented reality, and virtual realities by showcasing how AI can be leveraged to enhance our understanding of music and create immersive multimedia experiences.

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