  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.
