Adaptive hyperparameter tuning for time-frequency algorithms based on the zeros of the spectrogram

It is well known that the zeros of the spectrogram of white Gaussian noise are homogeneously distributed in the time-frequency (TF) plane. When a signal is present, large holes are created in this pattern. Recently, several methods have been proposed to detect and use them as masks for signal reconstruction. Yet, no systematic studies for tuning their hyperparameters on real data have been conducted. We investigate one key hyperparameter, the interaction scale r0, intuitively corresponding to the radius of a hole caused by a well-localized signal in the TF plane. We benchmark adaptive tuning protocols for r0 from the spatial statistics literature, which take as input simulated noise realizations and the actual data. We use non-Gaussian noise, audio signals, and the reconstruction performance is assessed with audio quality metrics. Results show that tuning strategies can depend both on the protocol and the data, highlighting the importance of benchmarking such approaches.