Spectral and dynamic analysis of audio compressors based on an opto compressor sample

This study comprehensively analyzes both hardware and software versions of opto (optical) compressor models, comparing the sound characteristics of the hardware version and various software emulations in detail using the Python programming language through the Librosa and Pyloudnorm libraries. Within the scope of the study, parameters such as the amount of dynamic compression, tonal changes, transient response, system noise increase, and spectral features of audio signals processed on both platforms were analyzed. During the analysis process, Python-based Librosa and Pyloudnorm libraries were integrated with additional Python tools such as Matplotlib and NumPy for data visualization and SciPy for statistical analysis. The results indicate significant differences between the processed audio signals of hardware and software models. Moreover, when examining the degree of similarity between software emulations and the hardware model, it was observed that these variables varied depending on the parameters and the test signal used. This study has developed a model for assessing the usability of Librosa and Pyloudnorm in Python for audio quality tests and has proposed a method for objectively evaluating the differences between audio files. Keywords: Audio Compressors, Spectral Analysis, Dynamic Analysis, Opto Compressors.