Testing a hybrid noise reduction technique effectiveness in a speech signal for a video conferencing system

The issue of audio signal quality during video conferences is considered. The effect of noise on the quality and intelligibility of the speech signal is described. The analysis of the noise reduction process in the audio signal in real time is carried out. The main approaches to solving the problem of noise reduction in real time are highlighted, namely the approach with recognition and elimination of noise and the approach with voice recognition and elimination of sounds different from the speech signal. An algorithm for intelligent noise reduction using artificial intelligence methods has been developed, characterized by the presence of mechanisms for adjusting the “intensity” of the noise reduction effect on the speech signal, as well as adjusting the “sensitivity” to noise, allowing to influence the efficiency of the algorithm for specific operating conditions without the need to retrain the neural network model of the algorithm. Software has been developed that implements a hybrid noise reduction technique in the form of a module that was implemented in a previously developed software package for organizing and conducting video conferences. Three typical scenarios have been identified, for each of which a reference (noiseless) speech signal has been selected, as well as a set of noises for these scenarios for conducting two types of tests in order to test the effectiveness of the noise reduction module in various conditions of its application. Experiments were carried out with a test of the effectiveness of noise reduction of noise segments during speech pauses using the methods of RMS signal volume and visual spectrogram analysis with calculation of the percentage of the number of samples of rhythms in the output signal. Experiments were carried out with a test of the effectiveness of noise reduction of a noisy speech signal using objective methods for evaluating the quality of the speech signal ViSQOL and NISQA. The test results are presented in the form of tables.