用迁移学习提升稀有女高音声部的声乐技术评估精度
Transfer Learning in Vocal Education: Technical Evaluation of Limited Samples Describing Mezzo-soprano
- 采用ImageNet和Urbansound8k预训练模型进行迁移学习
- 在小样本下平均准确率提升8.3%，最高达94.2%
- 构建专属的女中音声乐数据集MVS，推动量化教学
声乐教育因歌手个体差异和评分标准不一难以量化。深度学习虽具潜力，但针对罕见声部如女中音（Mezzo-soprano）的精准评估需大量标注数据支持。为此，本文利用在ImageNet和Urbansound8k上预训练的深度学习模型开展迁移学习，以提升声乐技术评估精度；同时，为解决样本不足问题，构建了专用数据集Mezzo-soprano Vocal Set (MVS)。实验结果表明，迁移学习使各模型整体准确率（OAcc）平均提升8.3%，最高达94.2%。本研究不仅提出一种新型女中音声乐技术评估方法，还为音乐教育提供了新的量化评价路径。
原文摘要 · Abstract (English)
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.
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