This paper aims to develop a holistic evaluation method for piano sound
quality to assist in purchasing decisions. Unlike previous studies that focused
on the effect of piano performance techniques on sound quality, this study
evaluates the inherent sound quality of different pianos. To derive quality
evaluation systems, the study uses subjective questionnaires based on a piano
sound quality dataset. The method selects the optimal piano classification
models by comparing the fine-tuning results of different pre-training models of
Convolutional Neural Networks (CNN). To improve the interpretability of the
models, the study applies Equivalent Rectangular Bandwidth (ERB) analysis. The
results reveal that musically trained individuals are better able to
distinguish between the sound quality differences of different pianos. The best
fine-tuned CNN pre-trained backbone achieves a high accuracy of 98.3\% as the
piano classifier. However, the dataset is limited, and the audio is sliced to
increase its quantity, resulting in a lack of diversity and balance, so we use
focal loss to reduce the impact of data imbalance. To optimize the method, the
dataset will be expanded, or few-shot learning techniques will be employed in
future research.

本研究旨在开发一种综合评估方法来辅助购买决策，评估不同钢琴的固有音质，使用主观问卷调查和卷积神经网络进行优化，结果显示受过音乐训练的人能够更好地区分不同钢琴的音质差异。