arXiv:2410.23325v1 Announce Type: cross
Abstract: Vocal education in the music field is difficult to quantify due to the individual differences in singers’ voices and the different quantitative criteria of singing techniques. Deep learning has great potential to be applied in music education due to its efficiency to handle complex data and perform quantitative analysis. However, accurate evaluations with limited samples over rare vocal types, such as Mezzo-soprano, requires extensive well-annotated data support using deep learning models. In order to attain the objective, we perform transfer learning by employing deep learning models pre-trained on the ImageNet and Urbansound8k datasets for the improvement on the precision of vocal technique evaluation. Furthermore, we tackle the problem of the lack of samples by constructing a dedicated dataset, the Mezzo-soprano Vocal Set (MVS), for vocal technique assessment. Our experimental results indicate that transfer learning increases the overall accuracy (OAcc) of all models by an average of 8.3%, with the highest accuracy at 94.2%. We not only provide a novel approach to evaluating Mezzo-soprano vocal techniques but also introduce a new quantitative assessment method for music education.
Deep Learning in Vocal Education: A Novel Approach to Evaluating Mezzo-soprano Vocal Techniques
Vocal education in the music field has always been a challenging endeavor, primarily due to the individual differences in singers’ voices and the subjective nature of evaluating singing techniques. However, recent advancements in deep learning offer an exciting opportunity to revolutionize music education by providing a quantitative analysis of vocal techniques. In this article, we explore the application of deep learning models in vocal technique evaluation and introduce a new method for assessing Mezzo-soprano vocal techniques.
One of the key advantages of deep learning is its ability to handle complex data and extract meaningful patterns from it. By leveraging this capability, we can train deep learning models on a diverse range of vocal samples, allowing them to learn the intricate nuances and subtleties of Mezzo-soprano singing. To achieve this, we employ transfer learning, a technique that utilizes pre-trained models on large datasets such as ImageNet and Urbansound8k.
Transfer learning enables us to fine-tune the pre-trained models to specialize in evaluating Mezzo-soprano vocal techniques. By retraining the models on a dedicated dataset called the Mezzo-soprano Vocal Set (MVS), we address the challenge of limited samples for rare vocal types. The MVS contains carefully annotated vocal recordings of Mezzo-soprano singers, providing a rich source of training data for our deep learning models.
Our experimental results demonstrate the effectiveness of transfer learning in improving the precision of vocal technique evaluation. We observed an average increase of 8.3% in the overall accuracy (OAcc) of all models, with the highest accuracy reaching an impressive 94.2%. These findings highlight the potential of deep learning to enhance vocal education by offering a quantitative and objective assessment of Mezzo-soprano vocal techniques.
This research aligns with the broader field of multimedia information systems, where the integration of various disciplines is essential for developing innovative solutions. The concepts explored in this study draw upon the fields of deep learning, where neural networks are trained on large datasets, and vocal education, where subjective assessments are traditionally used. By combining these disciplines, we create a multidisciplinary approach that bridges the gap between quantitative analysis and artistic expression.
Furthermore, this work has implications for other domains such as animations, artificial reality, augmented reality, and virtual realities, where realistic and expressive virtual characters are essential. The use of deep learning models for vocal technique evaluation can contribute to the development of more realistic and human-like virtual characters, enhancing the immersive experience in these virtual environments.
In conclusion, the application of deep learning in vocal education, particularly in evaluating Mezzo-soprano vocal techniques, offers promising avenues for advancing music education. By leveraging transfer learning and constructing dedicated datasets, we can improve the precision of vocal technique assessment and introduce a new quantitative assessment method. This research not only expands our understanding of deep learning but also demonstrates its potential to transform the field of music education and its interconnectedness with multimedia information systems.