GIO: A Timbre-informed Approach for Pitch Tracking in Highly Noisy Environments

Abstract

As one of the fundamental tasks in music and speech signal processing, pitch tracking has been attracting attention for decades. While a human can focus on the voiced pitch even in highly noisy environments, most existing automatic pitch tracking systems show unsatisfactory performance encountering noise. To mimic human auditory, a data-driven model named GIO is proposed in this paper, in which timbre information is introduced to guide pitch tracking. The proposed model takes two inputs: a short audio segment to extract pitch from and a timbre embedding derived from the speaker's or singer's voice. In experiments, we use a music artist classification model to extract timbre embedding vectors. A dual-branch structure and a two-step training method are designed to enable the model to predict voice presence. The experimental results show that the proposed model gains a significant improvement in noise robustness and outperforms existing state-of-the-art methods with fewer parameters.