Application of ABM to Spectral Features for Emotion Recognition

ER (Emotion Recognition) from speech signals has been among the attractive subjects lately. As known feature extraction and feature selection are most important process steps in ER from speech signals. The aim of present study is to select the most relevant spectral feature subset. The proposed method is based on feature selection with optimization algorithm among the features obtained from speech signals. Firstly, MFCC (Mel-Frequency Cepstrum Coefficients) were extracted from the EmoDB. Several statistical values as maximum, minimum, mean, standard deviation, skewness, kurtosis and median were obtained from MFCC. The next process of study was feature selection which was performed in two stages: In the first stage ABM (Agent-Based Modelling) that is hardly applied to this area was applied to actual features. In the second stageOpt-aiNET optimization algorithm was applied in order to choose the agent group giving the best classification success. The last process of the study is classification. ANN (Artificial Neural Network) and 10 cross-validations were used for classification and evaluation. A narrow comprehension with three emotions was performed in the application. As a result, it was seen that the classification accuracy was rising after applying proposed method. The method was shown promising performance with spectral features

Virtual Instrumentation for Speech Signal Processing in SMART Technology and Industry 4.0

Tato diplomová práce se zabývá automatickým rozpoznáváním řeči v oblasti Průmyslu 4.0 a SMART technologií pro následné testování vybraných filtračních metod. Nejprve se práce věnuje rešerši zabývající se uplatněním hlasového ovládání v konceptu Průmyslu 4.0 a SMART technologií. Dále se zabývá metodami automatického rozpoznávání řeči a filtračních metod. Primárně se tato práce zaměřuje na hlasové ovládání a filtraci rušení pomocí adaptivního algoritmu LMS a analýzy nezávislých komponent (ICA). V této práci byla realizována softwarová aplikace pro vytvoření databáze nahrávek rušení. Na základě těchto nahrávek byli realizovány tři vizualizace pro testování vybraných metod. Úspěšnost rozpoznání je vyhodnocena dle stavu rozpoznal/nerozpoznal, kdy každý příkaz byl 100x vysloven.This thesis deals with automatic speech recognition in Industry 4.0 and SMART technology for subsequent testing of selected filtration methods. Firstly, the thesis deals with the search of voice control in the Industry 4.0 and SMART technologies. It also deals with methods of automatic speech recognition and filtering methods. Primarily, this work focuses on voice control and interference filtering using adaptive LMS and Independent Component Analysis (ICA). In this work, a software application was created to create a jam recording database. Based on these recordings, three visualizations were made to test selected methods. Recognition success is evaluated by state recognized / not recognized when each command was 100x pronounced.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn