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The article presents an approach, methodology, the software system based on a machine learning technologies for convolutional neural network and its use for voice (cough) recognition. Tasks of article are receiving evaluating a voice detection system with deep learning, the use of a convolutional neural network and Python language for patients with cough. The convolutional neural network has been developed, trained and tested using various datasets and Python libraries. Unlike the existing modern works related to this area, proposed system was evaluated using a real set of environmental sound data, and not only on filtered or separated voice audio tracks. The final compiled model showed a relatively high average accuracy of 85.37 %. Thus, the system is able to detect the sound of a voice in a crowded public place, and there is no need for a sound separation phase for pre-processing, as other modern systems require. Several volunteers recorded their voice sounds using microphones of their smartphones, and it was guaranteed that they would test their voices in public places to make noise, in addition to some audio files that were uploaded online. The results showed an average recognition accuracy – of 85.37 %, a minimum accuracy – of 78.8 % and a record – of 91.9 %.

The subject of research is the development and implementation of Internet of Things (IoT) network structures for monitoring and analyzing audio information based on Raspberry microprocessor (MP) and an Arduino controller. The purpose of the article is to detail the process of developing an IoT based audio information monitoring network and evaluate the results. The authors have developed two variants of IoT structures for monitoring and analyzing audio and voice information. The IoT network includes a sound sensor (microphone), a unit for analyzing the information received from it and a decision-making module. A diagram of the first IoT structure for assessing the sound level based on the MP and controller is given.The algorithm of IoT network functioning for the analysis of voice information is detailed. It includes receiving information from the microphone, transmitting this information to the MP, processing it according to certain rules, forming a solution by the controller and issuing recommendations to a user. The algorithm is implemented in the IoT network, which includes a microphone, Raspberry MP, Arduino controller, software, applications for the operator.A prototype of the IoT network was created for the analysis of voice information and experiments were conducted to test its functioning. The audio recognizer was trained using various audio samples. The voice sound analysis system was tested in four scenarios: with a large and small amount of background noise, loud and quiet voice. Analysis of the results of the experiment showed that the voice sound analysis system works better when the voice is loud, as well as in a place where the situation is with minimal background noise.

The subject of research is modeling the structure of the Internet of things (IoT) network for controlling audio information based on the IoT platform. The purpose of the article is to detail the process of modeling the audio information monitoring network based on the IoT platform. The authors proposed the structure of a multi-agent system (MAS) for monitoring audio information (MASAI). The structure of MASAI includes many agents of sound transformation, analysis of information received from them, and decision-making. It was decided to use the IoT network, which includes sound sensors, the IoT platform, the notification service, and the user’s application to simulate the MASAI. The structure of this network using the Amazon platform is proposed. An algorithm for modeling the Internet of things network for analyzing audio information based on the AWS platform is presented, including simulating audio sensors, transmitting this information to the platform, sensors authenticating, processing information according to certain rules, generating notifications to a user. Detailed structure of the AWS platform is provided with a description of the functions of its components such as: device gateway, rule machine, certificate block, device copy block, database, analytics block, notification service. The algorithm for connecting devices to the AWS platform is given: creating a device certificate on the platform, creating a security policy, rules for processing information received from devices, and testing the network. The features of the algorithm for modeling the readings of sound information sensors on a smartphone are shown, steps are given for organizing its communication with the platform, performing security procedures, sending data in the form of an MQTT message, and displaying the captured audio information.

The processes of measurement, recording and analysis of different sound levels are considered. The amplitude and effect of sound waves vary considerably in continuous space-time measurements. Modeling different types of sounds and their spatiotemporal effects becomes important for assessing the sound situation both in working spaces and in recreation areas. Developing a model that reflects the characteristics of sounds, their sources, and the rules that govern their distribution in different environments would help track sound variations and predict their future changes for spatiotemporal states. Similar works abroad are given, but they are of a private nature. There are many features that you can use to describe audio signals. We consider a wide range of objects to evaluate the effect of each object and select the appropriate set of objects to distinguish between classes. Two estimations of a sound situation are given: on the basis of short-term energy and average speed of change. Three different classification methods are investigated: KNearest neighbors, Gaussian mixture model and Support vector machine.Multi-agent system (M)AS characteristics are given, the classification, trends in the use of multi-agent intelligent technologies for information processing are presented. Authors propose the use of MAS for sound information (MASSI) monitoring. MFSSI structure includes many agents of sound transformation, analysis of information received from them and decision-making. MASSI can handle noise levels in the urban space and to help in the study of noise pollution in many areas.