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Virtual reality (VR) is widely used for medical purposes, particularly, for assessment and treatment of phobic anxiety disorders. The article provides a review and critical analysis of methods and tools for assessing phobic anxiety disorders via registration and digital processing of physiological signals, and adaptation of various VR scenes to the user's actual state. The article presents a technique for studying phobic anxiety disorders in VR environment that enables VR scenes to be adapted for a particular user, considering his individual features, and behavior parameters. Adaptation of VR scenes is carried out by selecting an optimal set of hidden signal patterns registered with electrocardiogram and electroencephalogram signals.

Background. Monitoring the main indicators of human health in real time under conditions of performing especially dangerous professional activities is an urgent task of the digital health care system and medical instrumentation. Creating the multichannel monitoring systems will make it possible to quickly make a decision on the physiological, physical and psycho-emotional state of human health and provide a forecast for future periods. Materials and methods. The system being developed ensures the collection, transmission, processing and conceptual display of information about a critical state of health, namely, about the borderline level of blood pressure and heart rate, distance traveled (in the case of rescue operations), calorie expenditure and the level of physical activity of the person. Results. The presented system consists in the parallel registration of physiological signals from different users using tracker systems to a multichannel transmit / receive device using the BLU 5.0 open wireless communication standard. Conclusions. The article presents a multichannel system for monitoring the main indicators of the state of human health during the period of performing a particularly dangerous professional activity.

Speech/pause segmentation is classification of informative sections into voiced and unvoiced speech, and pauses. Accurate detection of the boundaries of the beginning and the end of informative sections of speech signals is one of the most important tasks in speech applications. The article presents a method for increasing the efficiency of speech/pause segmentation based on empirical mode decomposition. The proposed method is based on the use of decomposition in preprocessing of the original speech signals to form a set of new investigated signals containing the most reliable information about the boundaries of the beginning and the end of the sections of voiced and unvoiced speech, and pauses. The research has been carried out to evaluate the effect of the decomposition method and the duration of the fragments of the studied signals on the efficiency of speech/pause segmentation. The methods based on zero-crossing rate, short-time energy, and the analysis of one-dimensional Mahalanobis distance, were used for segmentation. The obtained research results have shown an increase in the efficiency of speech/pause segmentation by an average of 11.44 % for the first and second kind errors.

The article presents a method for speech/pause segmentation based on energy analysis of speech signal fragments using the Teager Energy Operator and Short-Time Energy. The nature of segmentation is a linear division of a speech signal into fragments, calculation of energy characteristic of fragments using the Teager Energy Operator, calculation of Short-Time Energy values for fragments of energy characteristic, and determining ‘speech/pause’ state of fragments based on the calculated threshold values of Short-Time Energy. The research on the proposed method was carried out, and the effectiveness of speech/pause segmentation was assessed in comparison with the method based on energy analysis using Short-Time Energy. In accordance with the obtained research results, it was concluded that due to the good susceptibility of the Teager Energy Operator to changes in signal amplitude and frequency, the proposed method provides the increased efficiency of speech/pause segmentation by 5.26 % and 5.51 % for the first and second kind errors, respectively. The proposed speech/pause segmentation method can be used for signal processing in real-time speech applications.

The article presents the improved method for speech signal segmentation, which provides an increase in the efficiency of detecting voiced and unvoiced sections, and pauses using the Teager energy operator. The method is based on energy analysis of speech signal fragments using the Teager energy operator with the subsequent analysis of zero-crossing rate and short-term energy of the energy characteristic function. The research was carried out to assess the performance and noise robustness of the proposed method in comparison with methods based on the analysis of zero-crossing rate, short-term energy, and one-dimensional Mahalanobis distance. In accordance with the obtained research results, it was concluded that due to the good susceptibility of the Teager energy operator to changes in signal amplitude and frequency, the improved method provides an enhancement of segmentation of speech signals, including noisy ones. Depending on the requirements for segmentation accuracy, the proposed method provides variability in the values of the first and second kind errors by changing the threshold coefficient.

The article presents a noise-robust method for speech/pause segmentation based on empirical mode decomposition. The method has been developed on the basis of a combined analysis of zero-crossing rate and short-term energy using empirical mode decomposition at the stage of preprocessing. Based on the results of preliminary processing, a set of new investigated signals, containing the most reliable information about the boundaries of the beginning and the end of informative sections of noisy speech, has been formed. The effect of the decomposition method and the influence of fragment duration of the investigated signals on the segmentation efficiency of noisy speech at different signal-to-noise ratio levels, from 20 to -5 dB with a step size of 5 dB, were assessed. The research results have shown a decrease in the values of the first and second kind errors during the segmentation of noisy speech signals using the proposed noise-robust method.

Segmentation into informative regions is an important stage in pre-processing of speech. The quality of segmentation affects the performance of almost all known applications of speech technologies (speech recognition, speaker identification, speech-to-text conversion, etc.). The article presents an improved speech/pause segmentation algorithm. The original algorithm is based on the use of probability density function of background noise, and the analysis of one-dimensional Mahalanobis distance of discrete timing for the investigated speech signal. Modernization consists in the fragmentation of speech and the decomposition of fragments into empirical modes for subsequent analysis of one-dimensional Mahalanobis distance of discrete timing for each mode separately. A study of the modernized algorithm has been carried out in comparison with the original algorithm and the well-known segmentation methods based on the analysis of zero-crossing rate and short-time energy. In accordance with the obtained results of the study, it was concluded that the improved segmentation algorithm provides the best detection of the boundaries of the beginning and the end of informative speech sections with the first and second kind errors, being 4.5767 % and 1.421 %, respectively.

Monitoring of the basic indicators of life activity of people in real time in conditions of performance of especially dangerous professional activity is an actual task of digital health care system of any industrial state. Logistical analysis of information about the physiological state of multiple users at the same time will allow the specialist / manager to make an operative correct decision on further actions of employees, their psycho-emotional level of disorder and the possibility of further performance of the task. The work presents a multi-channel system of reception/transfer of the main indicators of vital activity during the period of especially dangerous professional activity. The presented system collects, transmits, processes and conceptually displays information about critical state of health, namely, the boundary level of blood pressure and heart rate, distance traveled (in case of rescue work), calorie consumption and level of physical activity of a person. The essence of the system is the parallel recording of physiological signals from different users by means of tracker systems to a multichannel receiving/transmitting device through the open wireless communication standard BLU 5.0.

A method for voiced speech processing to be used in intelligent systems for recognizing human stressed states is proposed. Voiced speech fully reflects the violation in functioning of organs of human speech apparatus due to stressful emotional excitation of a person. The key feature of the method is to decompose voiced speech using the improved complete ensemble empirical mode decomposition with adaptive noise, to detect modes containing periodic information on the excitation source of the vocal tract, and to construct a composite signal reflecting information on glottal activity. The research results of the method, being computation of the pitch frequency of 100 multi-harmonic signals, with modulation in the frequency range of 0-2.5 Hz/ms in 0.5 Hz/ms increments, are presented. The modulation simulated the irregularity in vibration of the vocal folds (30-40 % of the nominal value) arising due to stressful emotional excitation. In accordance with the research results, it was concluded that the proposed method for voiced speech processing, using the improved decomposition, can be successfully tested in intelligent systems for recognizing stressed states in humans.

The article is devoted to technologies and virtual reality systems as comprehensive solutions for immersion of the user into virtual reality using specialized devices and interfaces. Technologies for wire/wireless transfer of audio-visual and parametric information in virtual reality systems are discussed. The aim of the article is to analyze and summarize advantages and disadvantages of modern facilities for multimedia and parametric information transfer used in virtual reality systems. A search method for research materials in Russian and international journals included in scientific citation databases was used. The paper analyzes the features (advantages and disadvantages) of using virtual reality in conditions of optical information transfer, wireless protocols (WiFi, Bluetooth, Wireless USB, LIDAR, ZigBee), and wire interfaces (Display Port, HDMI, USB) that provide user communication with virtual reality system. Virtual reality forms a new artificial real world transferred to the user via various wire/wireless (WiGig (802.11ad), WiFi 6 (802.11ax), WiHD (802.15.3c), and Display Port 2.0) interfaces, taking into account physiological, physical and psychometric indicators. Modern technical solutions should give impetus to the creation of adaptive virtual reality with a total immersion effect, when the user cannot distinguish a virtual world from real events.

A method for studying voiced speech by means of improved complete ensemble empirical mode decomposition with adaptive noise to detect stressful emotions in human-robot interaction is presented. The informative parameters of voiced speech fully reflect the violation in functioning of organs of human speech apparatus due to emotional excitation. The key feature of the research is to decompose a signal into empirical modes, to detect modes containing periodic information on the excitation source of the vocal tract, and to construct a composite signal reflecting information on glottal activity. The research results of voiced speech, being computation of the fundamental frequency of 100 multi-harmonic signals, are presented. The pitch frequency modulation of multi-harmonic signals in the frequency range of 0-2.5 Hz/ms in 0.5 Hz/ms increments simulated the irregularity in vibration of the vocal folds (30-40 % of the nominal value) arising from the emotional excitation of a person. The results obtained allow us to conclude that the presented method for the study of voiced speech, using the improved decomposition, can be successfully tested to detect stressful emotions in human interaction with robotic devices.

The article provides an integrated analysis of practical application of virtual reality (VR) in healthcare. Modern VR products have been created for the diagnosis and treatment of post-traumatic syndrome, Alzheimer’s disease, alcohol dependence, phobic disorders, and teenage depression. Information on such products is limited to simple scenes, without adaptation to the user. The paper shows a prospect for creating a virtual system with biological feedback. The proposed system is a set of portable and stationary devices for recording physiological signals and immersion into a virtual reality environment, as well as smart nodes for registration, processing and decision-making to correct the user’s mental state.

A comparative analysis of 18 popular speech databases used in the field of affective computing was carried out. A database containing unique audio recordings of Russian speech (words, phrases, and sentences), registered in natural experience of emotions by eyewitnesses and/or participants in adverse events under stress, has been developed. Voice recordings were taken from video materials of disasters and emergencies occurred on the territory of Russia in 2015-2019. Examination results of psychological and psychiatric reactions of 293 eyewitnesses and/or participants (119 men and 174 women, aged 15 to 65 years) to traumatic factors of adverse events are presented. In accordance with the development results, the following key conclusions have been made: in the future, the database will allow researchers to objectively assess the capabilities of existing methods and approaches for analyzing emotional speech, as well as to assess the need for new mathematical devices in speech signal processing; the voice database can be successfully used as training or test sample, necessary for effective training of systems for automatic recognition of stressful emotions in human-robot interaction.

An automated algorithm for pitch frequency measurement for diagnostic systems of borderline mental disorders is developed. It is based on decomposition of a speech signal into frequency components using an adaptive method for analyzing of non-stationary signals, improved complete ensemble empirical mode decomposition with adaptive noise (improved CEEMDAN), and isolating the component containing pitch. A block diagram for the developed algorithm and a detailed mathematical description are presented. A research of the algorithm using the formed verified signal base of healthy patients, and male and female patients with psychogenic disorders, aged from 18 to 60, is conducted. The research results are evaluated in comparison with the known algorithms for pitch frequency measurement. In accordance with the results of the study, the developed algorithm for pitch frequency measurement provides an accuracy increase in determination of borderline mental disorders: for the error of the first kind, on the average, it is more accurate by 10.7%, and for the second type error by 4.7%.

This paper describes a solution for borderline mental disorders detection. This approach is based on the ECG processing by Hilbert-Huang Transformation. The described approach allows to develop an additional module for mental disorders diagnostic systems. The research is based on the fact that in the conditions of borderline mental disorders there are changes in patients' heart function. Detection of significant ECG informative parameters is based on the effective and accurate measurement of amplitude, time, frequency and energy parameters of the ECG signal. A verified and registered database of 780 ECG signals of patients with borderline mental disorders and healthy people is used. The proposed method is described and the results are shown. The errors of the method with current sampling do not exceed 4%. The developed approach using volumetric spectral surfaces has showed a high probability of determining the period of occurrence of psycho-traumatic situations in various patients using the ECG.

An algorithm for pitch frequency measurement for pattern detecting systems of borderline mental disorders is developed. The essence of the algorithm is decomposition of a speech signal into frequency components using an adaptive method for analyzing of non-stationary signals, improved complete ensemble empirical mode decomposition with adaptive noise, and isolating the component containing pitch. A block diagram for the developed algorithm and a detailed mathematical description are presented. A research of the algorithm using the formed verified signal base of healthy patients, and male and female patients with psychogenic disorders, aged from 18 to 60, is conducted. The research results are evaluated in comparison with the known algorithms for pitch frequency measurement, realized on the basis of the autocorrelation function and its modifications, the robust algorithm for pitch tracking, and the sawtooth waveform inspired pitch estimation. In accordance with the results of the study, the developed algorithm for pitch frequency measurement provides an accuracy increase in determination of borderline mental disorders: for the error of the first kind, on the average, it is more accurate by 10.7%, and for the second type error by 4.7%.

Inductive sensors can be used in devices for measuring the composition of liquid media, the moisture content of bulk and solid materials, while they provide several advantages over the more common conductometric sensors, for example, they allow more accurate measurements of moving materials or ampoules of aggressive and toxic environments. Inductive sensors are less widely used due to the complexity of the design and the appearance of resonant effects in combination with the capacity of inductive sensors. The article discusses: equivalent circuits of inductive sensors represented by a multi-element equivalent circuit; options for constructing unifying single and multichannel converters of electrophysical properties parameters of substances based on an inductive sensor, their construction features, drawbacks, errors, the choice of an informative parameter, ways to increase the invariance of measurements. To process the obtained analog results from the outputs of the unifying converters, it is proposed to use digital post-processing in the microcontroller. This construction simplifies the scheme and allows you to create stand-alone mobile devices without the use of processing measurement results on a personal computer and without the use of specialized expensive software. The advantages of such a construction are its wide functionality and correction of additional errors.

The improved complete ensemble empirical mode decomposition with adaptive noise is proposed for solving speech signal processing tasks. Systematization of speech signal processing methods is shown structurally, demonstrating the variety of solution approaches. A method for segmenting speech into informative sections is presented to determine naturally expressed psycho-emotional states of a person. The key idea of the method is decomposition of speech, energy analysis of empirical modes, and determination of temporal informative parameters of voiced, unvoiced and pause sections. The method was investigated using the generated database of speech signals per 100 subjects, experiencing natural positive and negative emotions. The research results were evaluated in comparison with the known methods of segmentation. In accordance with the results of determination of psycho-emotional states, it was concluded that the proposed method more precisely defines the boundaries of informative sections, due to the advantages of the energy analysis of empirical modes obtained by the improved complete ensemble empirical mode decomposition with adaptive noise.

Background. The article is devoted to technologies and virtual reality systems as comprehensive solutions for immersion of the user into virtual reality using specialized devices and interfaces. Technologies for wire/wireless transfer of audio-visual and parametric information in virtual reality systems are discussed. The purpose of the article is to analyze and summarize advantages and disadvantages of modern facilities for multimedia and parametric information transmission used in virtual reality systems. Materials and methods. The research materials used the search method of scientific publications in Russian and international journals of scientific citation databases. Results. The paper analyzes the features (advantages and disadvantages) of using virtual reality in conditions of optical information transfer, wireless protocols (WiFi, Bluetooth, Wireless USB, LIDAR, ZigBee), and wire interfaces (Display Port, HDMI, USB) that provide user communication with virtual reality system. Virtual reality forms a new artificial real world transferred to the user via various wire/wireless (WiGig (802.11ad), WiFi 6 (802.11ax), WiHD (802.15.3c), and Display Port 2.0) interfaces, taking into account physiological, physical and psychometric indicators. Conclusions. Modern technical solutions should give impetus to the creation of adaptive virtual reality with a total immersion effect, when the user cannot distinguish a virtual world from real events.

An algorithm for adaptive segmentation to determine temporal patterns of speech signals reflecting human psycho-emotional states has been developed. The nature of the algorithm is the segmentation of speech into informative areas using adaptive decomposition and energy analysis of intrinsic mode functions, and determining patterns of temporal intervals of voiced, unvoiced, and pause sections. A research using the formed base of speech signals of 100 subjects, experiencing natural positive and negative emotions, was conducted. The research results were evaluated in comparison with the known methods of segmentation, followed by the determination of temporal patterns. In accordance with the results, the percentage of false assignments of the “normal” status to speech signals uttered by subjects under psycho-emotional excitement is greater in the known analogues than in the developed algorithm. This is also observed in false assignments of the “pathology” status to speech signals pronounced by subjects in a neutral state. Based on the determination results of psycho-emotional states, it was concluded that the developed adaptive algorithm more accurately determines the boundaries of informative areas, due to the advantages of the energy analysis of the modes obtained by the method of adaptive decomposition.

The paper proposes noise-robust method to determine prosodic characteristics of speech. The nature of the method consists in decomposing a speech signal into informative noise and signal frequency components by means of improved complete ensemble empirical mode decomposition with adaptive noise, and selection of the component containing the pitch with the subsequent determination of prosodic characteristics. Decomposition used in the method does not need any a priori information (presetting) on the analyzed noisy speech signals. The study was conducted using a verified base of pure and noisy speech signals recorded in 220 males and females, aged 18 to 79 years, with signs of psycho-emotional disorders. The results show that the algorithm provides good noise robustness of various intensities (signal-to-noise ratio is from 0 dB to 30 dB) and can be successfully tested in actual conditions of an “aggressive” noise environment for assessment of a human psycho-emotional state in free motor activity.

An automated noise-robust algorithm for determining prosodic characteristics of speech is proposed. The nature of the algorithm consists in decomposing a speech signal into informative noise and signal frequency components by means of improved complete ensemble empirical mode decomposition with adaptive noise, and selection of the component containing the pitch with the subsequent determination of prosodic characteristics. A study was conducted using a verified base of pure and noisy speech signals recorded in 220 males and females, aged 18 to 79 years, with signs of psycho-emotional disorders. In accordance with the results of the study, the algorithm provides good noise robustness of various intensities (signal-to-noise ratio is from 0 dB to 30 dB), and can be successfully tested in actual conditions of an “aggressive” noise environment for automated assessment of a human psycho-emotional state.

A method for automated segmentation to determine temporal patterns of speech signals reflecting naturally expressed psycho-emotional states has been developed. The nature of the method is the segmentation of speech into informative areas using adaptive decomposition and energy analysis of intrinsic mode functions, and determining patterns of temporal intervals of voiced, unvoiced, and pause sections. A research using the formed base of speech signals of 100 subjects, experiencing natural positive and negative emotions, was conducted. The research results were evaluated in comparison with the known methods of segmentation, followed by the determination of temporal patterns. In accordance with the determination results of psycho-emotional states, it was concluded that the developed automated method more accurately determines the boundaries of informative areas, due to the advantages of the energy analysis of empirical modes obtained by the method of improved complete ensemble empirical mode decomposition with adaptive noise.

A novel technology for speech signal segmentation into voiced, unvoiced and pause sections has been developed. It is based on the adaptive decomposition of short-time fragments of speech signals with a subsequent analysis of the parameters of empirical modes. A segmentation method based on a novel technology with a brief mathematical description is presented. The research has been conducted using the formed base of speech signals, recorded from 220 male and female subjects with signs of psycho-emotional disorders. In accordance with the obtained results, it was concluded that the method based on the developed technology determines the boundaries of informative sections more accurately, and it can be successfully tested at the preprocessing stages in the systems for detection and assessment of human psycho-emotional disorder.

Preprocessing of ECG signals allows increasing efficiency and effectiveness of diagnosis of socially significant heart diseases. Development of algorithms and programs for digital processing of ECG signals should ensure preservation of informatively significant components, adaptability to individual features and parameters of a patient, as well as the possibility of using online daily monitoring in mobile systems during human free movement activity. To solve the indicated problems, it is proposed to use the Hilbert-Huang transform, which will allow ensuring the reduction of the level of the most characteristic interference with minimal distortion of the useful component of the ECG signal.

The nature of the noise-robust processing method consists in decomposing a speech signal into informative noise and signal frequency components using the improved complete ensemble empirical mode decomposition with adaptive noise, and selection of the component containing the pitch with the subsequent determination of frequency features. A simplified block diagram of the method with a brief mathematical description is presented. A study was conducted using a verified base of pure and noisy speech signals recorded from a group of subjects with signs of psycho-emotional disorders. In accordance with the results of the study, the method provides good robustness to noise of various intensities (signal-to-noise ratio is from 0 dB to 30 dB), and can be successfully tested in actual conditions of an "aggressive" noise environment to assess a person’s psycho-emotional state.

The article is aimed to develop a modified Hilbert-Huang transform (HHT) for digital processing of medical signals. One of the main problems of the classical and well-known modifications of HHT is a high level of frequency mixing of the amplitude-time components of the studied medical signals. The use of the modified HHT with a level of frequency mixing of less than 5% will allow to quickly and efficiently separate frequency rhythms of electroencephalographic (EEG) signals for the purposes of functional diagnostics. This condition is significant for practical application in medical information systems at the stages of preliminary processing of the studied EEG.

The paper presents a method for assessing psycho-emotional state of control system operators with a high degree of responsibility. The nature of the method is an adaptive decomposition of a speech signal into frequency bands of equal articulation, using the improved complete ensemble empirical mode decomposition with adaptive noise, and determination of formant intelligibility, as well as frequency, width, level, and quality factor of formant regions in these bands using formant analysis. A mathematical description of the decomposition and a simplified block diagram of the proposed method are briefly presented. The study was conducted using the formed base of phonetically balanced speech signals recorded in healthy people, and people with psycho-emotional disorders, both male and female, aged from 18 to 79. According to the results of the study, the developed method provides a high detection accuracy of psycho-emotional disorders: the error of the first kind is 8.16%, and the error of the second kind is 5.66%.

The problem of increasing the precision with which the pitch frequency of speech signals is measured is considered. Existing algorithms for determining this frequency are presented and a new algorithm based on complementary ensemble empirical mode decomposition is developed. The results of the investigations confirm the robustness of the algorithm in the presence of frequency modulation of the pitch of speech signals.

Evaluation results of the developed method are presented. The mean energy value of the Hilbert spectrum of a speech signal obtained by the complementary ensemble empirical mode decomposition and the Hilbert–Huang transform is shown in different ranges depending on the choice of phrases formed by separate active organs of the speech apparatus.

The detection accuracy of psycho-emotional disorders depends on correct processing of speech signals. The main reason of low accuracy and large errors in measurements is associated with the use of inefficient and non-adaptive methods for processing of non-stationary speech signals. In this paper, the authors propose a method for increasing the detection efficiency of psycho-emotional disorders based on adaptive decomposition technology for non-stationary signals, namely, improved complete ensemble empirical mode decomposition with adaptive noise and mel-frequency cepstral analysis. A block diagram for the method and a brief mathematical description are presented. The research results are presented, on the basis of which it was concluded that the method proposed by the authors can successfully be tested in safety-critical control systems

A method for detecting psycho-emotional disorders, which is applicable to operators of safety-critical control systems, is proposed. The nature of the method is the decomposition of speech signals using the improved complete ensemble empirical mode decomposition with adaptive noise and formant analysis, namely, frequency and bandwidth measurement of the formant regions. A detailed mathematical description of decomposition varieties is presented, and a block diagram for the proposed method is given. The research of the method using the formed base of phonetically balanced speech signals registered in healthy people, and people under psycho-emotional disorders, both male and female, at the age of 18 to 79, is carried out. According to the results of the study, the developed method provides a high detection accuracy of psycho-emotional disorders: the first-kind error is 8.16 %, and the second kind error is 5.66 %.

The article discusses the issues of increasing the efficiency of diagnostics of psychogenic states by analyzing speech signals (SS). To increase the efficiency and effectiveness of SS processing, a technological process of recording-processing-analysis of signals in conditions of an increased level of interference and free motor activity of a person is proposed. It is proposed to use the Hilbert-Huang theory (HHT) as the basis of signal processing technology. The article analyzes the known theory and reveals its advantages and disadvantages. Based on the results of the technological process using the HHT theory, a method for determining psychogenic markers in SS is developed, studies are carried out, and conclusions of prospective studies are drawn.

A method for detecting psycho-emotional disorders, which is applicable to operators of safety-critical control systems, is proposed. The nature of the method is the decomposition of speech signals using the improved complete ensemble empirical mode decomposition with adaptive noise and formant analysis, namely, frequency and bandwidth measurement of the formant regions. A detailed mathematical description of decomposition varieties is presented, and a block diagram for the proposed method is given. The research of the method using the formed base of phonetically balanced speech signals registered in healthy people, and people under psycho-emotional disorders, both male and female, at the age of 18 to 79, is carried out. According to the results of the study, the developed method provides a high detection accuracy of psycho-emotional disorders: the first-kind error is 8.16%, and the second kind error is 5.66%.

A method for speech commands processing, which increases the effectiveness of assessing a psycho-emotional state of operators of medical exoskeletons, is proposed. The method uses an adaptive processing method of empirical mode decomposition, and determines informative parameters relevant to a psycho-emotional component of speech commands.

The article considers an urgent problem of a subband decomposition of speech signals into non-overlapping frequency bands. A brief overview of known decomposition methods based on the Fourier transform and wavelet transform is presented. Their advantages and disadvantages are considered. The prospects for application of a new method of empirical mode decomposition are revealed. A detailed mathematical description of various decomposition methods is presented, and the relevance of automation of improved complete ensemble empirical mode decomposition with adaptive noise to increase the efficiency of speech signal processing is emphasized. A method for automated control of the improved decomposition for measuring pitch frequency, and a brief mathematical description are presented. The investigations of the method aimed at determining optimum operational settings of the improved decomposition for increasing the frequency-selective properties are carried out. In accordance with the results of the research, it is concluded that the method proposed by the authors successfully solves the problem of automating the process of the improved decomposition to increase the accuracy of the pitch frequency measurement.