Improved CEEMDAN Based Speech Signal Analysis Algorithm for Mental Disorders Diagnostic System

2019 ◽  
Vol 10 (1) ◽  
pp. 22-47 ◽  
Author(s):  
Alan K. Alimuradov ◽  
Alexander Yu. Tychkov ◽  
Andrey V. Kuzmin ◽  
Pyotr P. Churakov ◽  
Alexey V. Ageykin ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Speech Signal ◽  
Block Diagram ◽  
Diagnostic System ◽  
Frequency Measurement ◽  
Adaptive Method ◽  
Ensemble Empirical Mode Decomposition ◽  
Diagnostic Systems ◽  
Mode Decomposition ◽  
Pitch Frequency

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%.

EEMD-Based Speaker Emotional Analysis for Speech Signal

2011 ◽  
Vol 121-126 ◽  
pp. 815-819 ◽  
Author(s):  
Yu Qiang Qin ◽  
Xue Ying Zhang
Keyword(s):  
Emotion Recognition ◽  
Empirical Mode Decomposition ◽  
Speech Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
New Method ◽  
Speech Signals ◽  
Emotional Speech ◽  
Mode Decomposition ◽  
Two Parameters ◽  
Mode Mixing

Ensemble empirical mode decomposition(EEMD) is a newly developed method aimed at eliminating mode mixing present in the original empirical mode decomposition (EMD). To evaluate the performance of this new method, this paper investigates the effect of two parameters pertinent to EEMD: the emotional envelop and the number of emotional ensemble trials. At the same time, the proposed technique has been utilized for four kinds of emotional(angry、happy、sad and neutral) speech signals, and compute the number of each emotional ensemble trials. We obtain an emotional envelope by transforming the IMFe of emotional speech signals, and obtain a new method of emotion recognition according to different emotional envelop and emotional ensemble trials.

scholarly journals Speech Endpoint Detection in Noisy Environment Based on the Ensemble Empirical Mode Decomposition

2011 ◽  
Vol 2-3 ◽  
pp. 135-139
Author(s):  
Jing Jiao Li ◽  
Dong An ◽  
Jiao Wang ◽  
Chao Qun Rong
Keyword(s):  
Empirical Mode Decomposition ◽  
Speech Signal ◽  
Recognition System ◽  
Ensemble Empirical Mode Decomposition ◽  
Endpoint Detection ◽  
Noisy Environment ◽  
Low Snr ◽  
Voice Signal ◽  
Mode Decomposition ◽  
Speech Endpoint Detection

Speech endpoint detection is one of the key problems in the practical application of speech recognition system. In this paper, speech signal contained chirp is decomposed into several intrinsic mode function (IMF) with the method of ensemble empirical mode decomposition (EEMD). At the same time, it eliminates the modal mix superposition phenomenon which usually comes out in processing speech signal with the algorithm of empirical mode decomposition (EMD). After that, selects IMFs contained major noise through the adaptive algorithm. Finally, the IMFs and speech signal contained chirp are input into the independent component analysis (ICA) and pure voice signal is separated out. The accuracy of speech endpoint detection can be improved in this way. The result shows that the new speech endpoint detection method proposed above is effective, and has strong anti-noises ability, especially suitable for the speech endpoint detection in low SNR.

Psychodynamic diagnostic manual in clinical practice-case report

2011 ◽  
Vol 26 (S2) ◽  
pp. 1027-1027
Author(s):  
S. Kecojevic Miljevic
Keyword(s):  
Clinical Practice ◽  
Mental Disorders ◽  
Collaborative Work ◽  
Diagnostic System ◽  
Diagnostic Systems ◽  
Therapeutic Approaches ◽  
Icd 10 ◽  
Dsm Iv ◽  
Mind Set ◽  
Psychodynamic Diagnostic Manual

Psychodynamic diagnostic manual was created by collaborative work of organisatios in the field of mental health and an authorial group with the aim of supplementing currently valid diagnostic systems ICD-10 and DSM-IV-TR. PDM is based on traditional psychoanalytical and psychodynamic concepts of genesis of mental disorders, currently valid diagnostic systems, new insights in the area of neurosciences, as well as on the evaluation of outcomes of different therapeutic approaches. The concept of mental disorders understanding adopted by PDM is bio-psycho-social and it follows a primary course of topical trends dictated by World Psychiatric Association towards personality orientated psychiatry. The purpose of this paper is the usage presentation of the useful guide in clinical practice with the aim of diagnosing mental disorder in the case of the described patient of a type of psychoanalytical approach applied in her treatment. The multi-axis diagnostic system of PDM has been used in the methodology of this paper. Based on this research we conclude that the described patient suffers from somatisation personality disorder, she also possesses the level of mental functioning with the moderate to higher degree of limitation, and symptomatically demonstrates somatiform disorder from the class of gastrointestinal system dysfunction and anxious disorder from a class of phobia.The mind-set of patient, and the limited level of her mental functions which suggests the inclination to a borderline level of type of personality organisations indicate plausible grounds for using supportive expressive psychoanalytical psychoterapeutic approach.

scholarly journals The extraction of informative vibration components of bearings by different adaptive methods

2021 ◽  
Author(s):  
Serhii Mykhalkiv
Keyword(s):  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Adaptive Methods ◽  
Adaptive Method ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Adaptive Noise ◽  
Impulse Modulation

It was suggested to select the best adaptive method after proper comparative researches, for the extraction of informative vibration components of bearings. The description and drawbacks of empirical mode decomposition method were presented, and the properties of improved ensemble empirical mode decomposition method and complete ensemble empirical mode decomposition with adaptive noise method were highlighted. A simulated additive signal contained impulse, modulation components and two sinusoids. The extracted intrinsic mode functions were the decomposition results of the first two adaptive methods, which failed to separate impulse and modulation components. Meanwhile, the intrinsic mode functions of the third adaptive method had separately impulse and modulation components, and the method proved to be effective in the separation of the vibration components during the vibrodiagnostics of bearings and gearboxes of the industrial equipment.

Adaptive method for SNR estimation in speech signal

1996 ◽  
Vol 32 (5) ◽  
pp. 421 ◽  
Author(s):  
J. Rodriguez ◽  
F. Rios ◽  
R. Escaño-Quero ◽  
J.F. Martin
Keyword(s):  
Speech Signal ◽  
Adaptive Method ◽  
Snr Estimation
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