Method of separation of noise in spectrograms from real data using threshold binarization

Spectral analysis is an important tool in many scientific and technical disciplines, such as radio engineering,acoustics, telecommunications, and medical diagnostics. However, one of the main challenges that reduces theeffectiveness of this analysis is the presence of noise components in the spectrogram, which can significantly distort theresults and complicate further signal processing. The presence of noise, which has different characteristics, cannegatively affect the accuracy of identifying the useful signal, creating additional difficulties in its interpretation anduse. Traditional filtering methods are not always effective in removing noise components, especially when they have acomplex structure or vary in real-time. In particular, conventional filters may not account for local variations in thenoise level, which leads to a decrease in the quality of the processed data. Therefore, the search for new methods foreffectively extracting the useful signal and filtering noise remains a relevant task.This article proposes a new method for processing spectrograms based on threshold binarization, which allowsfor the efficient isolation of active signals and the removal of background noise components. The algorithmimplementation utilizes a combination of two powerful image processing libraries, SkiaSharp and OpenCV. Thisprovides not only flexibility and high performance during spectrogram processing but also adaptability to variousspectral environment conditions. The proposed method significantly reduces the impact of random fluctuations in thenoise level, improving the accuracy of analysis and the reliability of results. The use of threshold binarization withadaptive threshold value determination enables the preservation of important frequency components of the signal while effectively removing unnecessary noise elements. The application of this technology can substantially improve real-timespectral analysis, providing increased resilience to environmental changes.Thus, the proposed approach holds promise for further application in various fields where precise frequencycharacteristic analysis of signals is crucial. Furthermore, the method can be used to improve the quality of spectralmonitoring, acoustic analysis, as well as in signal recognition tasks within radio-electronic systems.