The lecture recital was given June 29, 1976. The subject was the History of the Violin Scordatura, and it included an historical survey of scordatura compositions along with discussions of the problems of notation and hints for solving the practical problems of performance. Works by Biber, Vivaldi, and Nardini were performed at appropriate moments during the lecture. In addition to the lecture recital, three other public recitals were performed: The first solo recital was on April 15, 1974 and included works of Albinoni, Copland, and Beethoven. The second program, a chamber music recital on January 19, 1975, featured works for piano trio by Haydn, Copland, and Arensky. The third recital was presented on April 14, 19 75 and included works by Vitali, Prokofiev, and Grieg. Magnetic tape recordings of all four programs and the written lecture material are filed together as the dissertation.

This thesis addresses the question of mathematical involvement in music, a topic long discussed going all the way back to Plato. It details the mathematical construction of the three main tuning systems (Pythagorean, just intonation, and equal temperament), the methods by which they were built and the mathematics that drives them through the lens of a historical perspective. It also briefly touches on the philosophical aspects of the tuning systems and whether their differences affect listeners. It further details the invention of the Fourier Series and their relation to the sound wave to explain the concept of overtones within the tuning systems.

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<p> A proposal is made to extend the present capabilities of gun-launched rocket vehicles to include attitude control during flight. The problems involved are stated and design criteria for possible sensors are listed. A review of presently available sensing devices is made and rejection of unsuitable instruments is based on fundamentals of their design and operation. </p> <p> A report is made upon the sensors which most adequately fulfil the harsh environmental requirements of gun-launch. These sensors are infrared-horizon sensors and a tuning fork vibratory gyroscope. A preliminary design is given for the tuning fork gyroscope a well as a summary of fundamental design considerations. </p> / Thesis / Master of Engineering (ME)

The human auditory system can become an active agent in the production of sound when stimulated with specific tone combinations. The resulting auditory distortion products can be amplified and drawn attention to by employing certain just intonation practices – a compositional technique that may serve as a powerful catalyst for reaching different states of mind in listeners. In this thesis, those psychoacoustic phenomena critical to the act of tuning are explored. An experiential tuning protocol is introduced and supported by insights from physiology and neuroscience research. Based on the tuneability of musical intervals, a harmonic framework involving harmonic and subharmonic relationships is analyzed and exemplified via the accompanying composition XV XXVII III XXI IX: Variations. From a phenomenological perspective, the research extends to broader contexts, investigating potential social, ethical, cultural, and political implications of such a practice. / <p><em>XV XXVII III XXI IX: Variations</em> by Luka Aron is a work in 5 parts in which a selected acoustic instrumentation, consisting of bass clarinet, contrabass, euphonium, foghorn organ, harpsichord, serpent, shō, and trumpet coalesces with analog as well as digital synthesis, into one unified mass of sound. By tuning sustained tones towards precision upon occupying the same pitch space, the various timbresare as much canceled out as reinforced, resulting in flux states of spectral fusion. This effect is further achieved through traversing a labyrinth of multiple closely related overtone series, serving as a harmonic framework for the piece’s ever-shifting bedrock. A careful selection of partials is presented, stimulating additional tones in the perception of the listener, making use of a psychoacoustic phenomenon, commonly referred to as otoacoustic emissions, or combination tones. Heavy distortion is then applied to the source signal, allowing the combination tones to materialize in the physical space. Through this, a secondary structure (that, in fact, exposes theundertone series) is gradually unveiled: like light rays meeting the surface of water, partially reflecting back to air, and refracting at once, as they pass from one medium to the other. On a structural plane, the piece stems from complex multi-layered golden mean relationships that permeate to all levels of the composition, ranging from the overall arc to the formal as well as rhythmical aspects of each individual variation, where every sound event is spiraling out of the previous one, and the endpoint is determined right at the initial stroke, before ever unfolding over the total playtime of 43 minutes. With Aron operating the Buchla 200 and EMS VCS3 synthesizers, in addition to the SuperCollider andPure Data coding environments, the cast of musicians includes an array of Stockholm-based artists, such as Mattias Hållsten on shō–a japanese mouth organ–and Susana Santos Silva on trumpet (both members of CC Hennix’ Kamigaku ensemble), just intonation contrabassist Vilhelm Bromander, along with Amina Hocine and her unique self-built foghorn organ. Frequent collaborators Fabian Willmann on bass clarinet and Raphaël Rossé on serpent and euphonium join from the electroacoustic group MINUA, which Aron co-founded. For this occasion, the piece will be diffused on an arrangement of 31 speakers.</p><p><strong>Den klingande delen är arkiverad.</strong></p><p><strong>Luka Aron är artistnam för Luca Aaron Pusch.</strong></p>

Using the data collected during the hyperparameter tuning for Google's Dependency Parser Parsey McParseface, Feedforward neural networks and the correlation between its hyperparameter during the networks training are explained and analysed in depth.:1 Introduction to Neural Networks 4 1.1 History of AI 4 1.2 The role of Neural Networks in AI Research 6 1.2.1 Artificial Intelligence 6 1.2.2 Machine Learning 6 1.2.3 Neural Network 8 1.3 Structure of Neural Networks 8 1.3.1 Biology Analogy of Artificial Neural Networks 9 1.3.2 Architecture of Artificial Neural Networks 9 1.3.3 Biological Model of Nodes – Neurons 11 1.3.4 Structure of Artificial Neurons 12 1.4 Training a Neural Network 21 1.4.1 Data 21 1.4.2 Hyperparameters 22 1.4.3 Training process 26 1.4.4 Overfitting 27 2 Natural Language Processing (NLP) 29 2.1 Data Preparation 29 2.1.1 Text Preprocessing 29 2.1.2 Part-of-Speech Tagging 30 2.2 Dependency Parsing 31 2.2.1 Dependency Grammar 31 2.2.2 Dependency Parsing Rule-Based & Data-Driven Approach 33 2.2.3 Syntactic Parser 33 2.3 Parsey McParseface 34 2.3.1 SyntaxNet 34 2.3.2 Corpus 34 2.3.3 Architecture 34 2.3.4 Improvements to the Feed Forward Neural Network 38 3 Training of Parsey’s Cousins 41 3.1 Training a Model 41 3.1.1 Building the Framework 41 3.1.2 Corpus 41 3.1.3 Training Process 43 3.1.4 Settings for the Training 44 3.2 Results and Analysis 46 3.2.1 Results from Google’s Models 46 3.2.2 Effect of Hyperparameter 47 4 Conclusion 63 5 Bibliography 65 6 Appendix 74

This thesis develops a method for using machine learning in a industrial pro-cess. The implementation of this machine learning model aimed to reduce costsand increase efficiency of train wheel overhaul in partnership with the AustrianFederal Railroads, Oebb. Different machine learning models as well as categoryencodings were tested to find which performed best on the data set. In addition,differently sized training sets were used to determine whether size of the trainingset affected the results. The implementation shows that Oebb can save moneyand increase efficiency of train wheel overhaul by using machine learning andthat continuous training of prediction models is necessary because of variationsin the data set.

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