  This paper presents a comprehensive study of automatic performer
identification in expressive piano performances using convolutional neural
networks (CNNs) and expressive features. Our work addresses the challenging
multi-class classification task of identifying virtuoso pianists, which has
substantial implications for building dynamic musical instruments with
intelligence and smart musical systems. Incorporating recent advancements, we
leveraged large-scale expressive piano performance datasets and deep learning
techniques. We refined the scores by expanding repetitions and ornaments for
more accurate feature extraction. We demonstrated the capability of
one-dimensional CNNs for identifying pianists based on expressive features and
analyzed the impact of the input sequence lengths and different features. The
proposed model outperforms the baseline, achieving 85.3% accuracy in a 6-way
identification task. Our refined dataset proved more apt for training a robust
pianist identifier, making a substantial contribution to the field of automatic
performer identification. Our codes have been released at
https://github.com/BetsyTang/PID-CNN.
