This thesis presents tools and components necessary to further develop an implementation of a method. The method attempts to use the non invasive electroglottographic signal to locate rapid transitions between voice registers. Implementations for sample entropy and the Discrete Fourier Transform (DFT) implemented for the programming language SuperCollider are presented along with tools necessary to evaluate the method and present the results in real time. Since different algorithms have been used, both for clustering and cycle separation, a comparison between algorithms for both of these steps has also been done. / Denna rapport presenterar verktyg och komponenter som är nödvändiga för att vidareutveckla en implementation av en metod. Metoden försöker att använda en icke invasiv elektroglottografisk signal för att hitta snabba övergångar mellan röstregister. Det presenteras implementationer för sampelentropi och den diskreta fourier transformen för programspråket SuperCollider samt verktyg som behövs för att utvärdera metoden och presentera resultaten i realtid. Då olika algoritmer har använts för både klustring och cykelseparation så har även en jämförelse mellan algoritmer för dessa steg gjorts.

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The human voice is a product of an intricate biophysical system. The complexity of this system enables a rich variety of possible sounds, but at the same time poses great challenges for quantitative voice analysis. For example, the vocal folds can vibrate in several different ways, leading to variations in the acoustic output. Because the vocal folds are relatively inaccessible, such variations are often difficult to account for. This work proposes a novel method for extracting non-invasively information on the vibratory state of the human vocal folds. Such information is important for creating a more complete voice analysis scheme. Invasive methods are undesirable because they often disturb the subjects and/or the studied phenomena, and they are also impractical in terms of accessibility and cost. A useful frame of reference for voice analysis is the Voice Range Profile (VRP). The 3 dimensional form of the VRP can be used to depict any phonatory metric over the 2 dimensional plane defined by the fundamental frequency of phonation (x-axis) and the sound pressure level (y-axis). The primary goal of this work was to incorporate information on the vibratory state of the vocal folds into the Voice Range Profile (e.g., as a color change). For this purpose, a novel method of analysis of the electroglottogram (EGG) was developed, using techniques from machine learning (clustering) and nonlinear time series analysis (sample entropy estimation). The analysis makes no prior assumptions on the nature of the EGG signal and does not rely on its absolute amplitude or frequency. Unlike time-domain methods, which typically define thresholds for quantifying EGG cycle metrics, the proposed method uses information from the entire cycle of each period. The analysis was applied in a variety of experimental conditions (constant vowel with different vibratory states, constant vibratory state and different vowels, constant vowel and vibratory state with varying lung volume) and the magnitude of effect on the EGG short-term spectrum was estimated for each of these conditions. It was found that the short-term spectrum of the EGG signal sufficed to discriminate between different phonatory configurations, such as modal and falsetto voice. It was found also that even supposedly purely articulatory changes could be traced in the spectrum of the EGG signal. Finally, possible pedagogical and clinical applications of the method are discussed. / <p>QC 20140609</p> / FonaDyn

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This master thesis describes early attempts at using electroglottography (EGG) to capture such changes in vocal fold vibration patterns that could be of interest to teachers of contemporary commercial music. After initial explorations, focus is placed on detecting potentially detrimental phonation; more specifically on the pressed quality often associated with loud singing in high register (belting). FonaDyn, a program written in the SuperCollider language, is used to detect pressedness using an algorithm based on K-means clustering of Fourier components of EGG cycles. Results indicate that pressedness affects phonation in ways detectable using EGG. Changes caused by pressedness seem to vary between registers and this variation is similar between subjects. Detection of pressedness in a subject is quite successful when training the algorithm on the same subject, but not always across subjects. / Denna masteruppsats beskriver inledande försök att använda elektroglottografi (EGG) för att avläsa sådana förändringar i stämbandens vibrationsmönster som skulle kunna vara av intresse för sånglärare inom icke-klassisk stil. Tidiga undersökningar leder till att fortsatt fokus läggs på att detektera fonationstyper som kan orsaka röstskador; mer specifikt den typ av pressad röstkvalitet som ofta förknippas med stark sång i högt register (s.k. belting). FonaDyn, ett datorprogram skrivet i språket SuperCollider, används för att detektera pressad fonation med hjälp av K-means-klustring av EGG-cykler baserat på deras Fourierkomponenter. Resultaten indikerar att pressad fonation går att urskilja med hjälp av EGG. Kännetecknen för pressad fonation tycks skilja sig mellan röstregister och denna skillnad är snarlik hos olika försökspersoner. Programmet klarar av att känna igen pressad fonation hos samma person som algoritmen tränats på men inte alltid om algoritmen tränats på en annan sångare.

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