PiVoD: Pitch, Volume and Duration Invariant Representation for Music Tone Quality Evaluation
Yixin Wang, Xiaohong Guan, Youtian Du, Chenxu Wang, Xiaobing Li, Yu Pan · IEEE Transactions on Audio Speech and Language Processing · 2025
Tone quality is of pivotal importance in the auditory perception of musical performance. Depending on the performer and the instrument, tone quality evaluation is subjective and time-consuming, with inherent difficulties stemming from the absence of precise measurement methods. In this study, we develop a novel method for tone quality evaluation utilizing an adversarial domain-invariant learning strategy to construct a representation invariant to changes in pitch, volume, and duration. The wide-band Mel frequency cepstral coefficients are employed for pitch-invariant feature extraction and instance normalization for volume invariance. An adversarial-trained time-delay neural network encoder is developed for enhancing pitch and duration invariance via random pitch shift and temporal segmentation. Experiments conducted on our curated dataset and the Good-sound dataset show that significant improvements from the new method are achieved in evaluating tone quality ascribed to performers and instruments, yielding a 15.3% and 9.5% increase in classification accuracy, respectively, compared to classical feature-based techniques. Remarkably, the class-wise outcomes exhibit enhancements in F-scores of 33.6% and 9.8% for each respective dataset. Ablation studies on pitch, volume, and duration invariance further underscore the efficacy of our approach. This substantial enhancement over existing methods presents a novel perspective on tone quality representation and offers a practical resource for music performance analysis.