MF-PAM: Accurate Pitch Estimation through Periodicity Analysis and Multi-level Feature Fusion
| Author | Woo-Jin Chung, Doyeon Kim, Soo-Whan Chung, Hong-Goo Kang |
| Publication | INTERSPEECH |
| Year | 2023 |
| Link | [Paper] [arXiv] [Github] |
ABSTRACT
We introduce Multi-level feature Fusion-based Periodicity Analysis Model (MF-PAM), a novel deep learning-based pitch estimation model that accurately estimates pitch trajectory in noisy and reverberant acoustic environments. Our model leverages the periodic characteristics of audio signals and involves two key steps: extracting pitch periodicity using periodic non-periodic convolution (PNP-Conv) blocks and estimating pitch by aggregating multi-level features using a modified bi-directional feature pyramid network (BiFPN). We evaluate our model on speech and music datasets and achieve superior pitch estimation performance compared to state-of-the-art baselines while using fewer model parameters. Our model achieves 99.20% accuracy in pitch estimation on a clean musical dataset. Overall, our proposed model provides a promising solution for accurate pitch estimation in challenging acoustic environments and has potential applications in audio signal processing.