The field of signal processing and audio analysis is witnessing significant advancements with the development of innovative methods and techniques. Researchers are focusing on improving the accuracy and efficiency of existing algorithms, such as pitch tracking and audio fingerprinting. The use of deep learning and neural networks is becoming increasingly prevalent, enabling the creation of more robust and adaptable models. Furthermore, the integration of different mathematical transforms, such as the Fourier and Sumudu transforms, is leading to more effective solutions for complex problems. Notable papers in this area include: A Robust Method for Pitch Tracking in the Frequency Following Response using Harmonic Amplitude Summation Filterbank, which proposes a novel method for pitch tracking in the frequency following response. PeakNetFP: Peak-based Neural Audio Fingerprinting Robust to Extreme Time Stretching, which introduces a lightweight and efficient neural audio fingerprinting system. Maximal Update Parametrization and Zero-Shot Hyperparameter Transfer for Fourier Neural Operators, which enables the transfer of optimal hyperparameters across models with different numbers of Fourier modes.