Cognitively-motivated geometric methods of pattern discovery and models of similarity in music

This thesis is concerned with cognitively-motivated representations of musical structure. Three problems are addressed, each related in terms of their focus on music as an object of perception, and in the application of geometrical methods of knowledge representation. The problem of pattern discovery in discrete representations of polyphonic music is first considered, and a heuristic proposed which seeks to assist musicological analysis by identifying patterns that may be salient in perception, from a large number of potential patterns. This work is based on geometric principles that are far removed from plausible psychological models of pattern induction, but the method is motivated by psychological evidence for the importance of invariance and repetition in perception. The second and third problems explicitly adopt a cognitive theory of representation, namely the conceptual space framework developed by Gärdenfors (2000). Within this framework, concepts can be represented geometrically within perceptually grounded quality dimensions, and where distance in the space corresponds to similarity. The second problem concerns the prediction of melodic similarity, and the theory of conceptual spaces is investigated in the novel context of point set representations of melodic structure, employing the Earth Mover's Distance metric (Rubner 2000). This work builds on the work of Typke (2007) concerning the application of Earth Mover's Distance to melodic similarity. Evaluation is performed with respect to published psychological data (Müllensiefen 2004), and the MIREX 2005 symbolic melodic similarity evaluation. The third problem concerns the conceptual representation of metrical structure, informed by the psychological theory of metre developed by London (2004). A symbolic formalisation of this theory is developed, alongside two geometrical models of metrical-rhythmic structure, which are evaluated within a genre classification task

Towards a Computational Model of Musical Accompaniment: Disambiguation of Musical Analyses by Reference to Performance Data

Institute of Perception, Action and BehaviourA goal of Artificial Intelligence is to develop computational models of what would be considered intelligent behaviour in a human. One such task is that of musical performance. This research specifically focuses on aspects of performance related to the performance of musical duets. We present the research in the context of developing a cooperative performance system that would be capable of performing a piece of music expressively alongside a human musician. In particular, we concentrate on the relationship between musical structure and performance with the aim of creating a structural interpretation of a piece of music by analysing features of the score and performance. We provide a new implementation of Lerdahl and Jackendoff’s Grouping Structure analysis which makes use of feature-category weighting factors. The multiple structures that result from this analysis are represented using a new technique for representing hierarchical structures. The representation supports a refinement process which allows the structures to be disambiguated at a later stage. We also present a novel analysis technique, based on the principle of phrase-final lengthening, to identify structural features from performance data. These structural features are used to select from the multiple possible musical structures the structure that corresponds most closely to the analysed performance. The three main contributions of this research are:1- An implementation of Lerdahl and Jackendoff’s Grouping Structure which includes feature-category weighting factors; 2- A method of storing a set of ambiguous hierarchical structures which supports gradual improvements in specificity; An analysis technique which, when applied to a musical performance, succeeds 3- in providing information to aid the disambiguation of the final musical structure. The results indicate that the approach has promise and with the incorporation of further refinements could lead to a computer-based system that could aid both musical performers and those interested in the art of musical performance

Measuring Expressive Music Performances: a Performance Science Model using Symbolic Approximation

Music Performance Science (MPS), sometimes termed systematic musicology in Northern Europe, is concerned with designing, testing and applying quantitative measurements to music performances. It has applications in art musics, jazz and other genres. It is least concerned with aesthetic judgements or with ontological considerations of artworks that stand alone from their instantiations in performances. Musicians deliver expressive performances by manipulating multiple, simultaneous variables including, but not limited to: tempo, acceleration and deceleration, dynamics, rates of change of dynamic levels, intonation and articulation. There are significant complexities when handling multivariate music datasets of significant scale. A critical issue in analyzing any types of large datasets is the likelihood of detecting meaningless relationships the more dimensions are included. One possible choice is to create algorithms that address both volume and complexity. Another, and the approach chosen here, is to apply techniques that reduce both the dimensionality and numerosity of the music datasets while assuring the statistical significance of results. This dissertation describes a flexible computational model, based on symbolic approximation of timeseries, that can extract time-related characteristics of music performances to generate performance fingerprints (dissimilarities from an ‘average performance’) to be used for comparative purposes. The model is applied to recordings of Arnold Schoenberg’s Phantasy for Violin with Piano Accompaniment, Opus 47 (1949), having initially been validated on Chopin Mazurkas.1 The results are subsequently used to test hypotheses about evolution in performance styles of the Phantasy since its composition. It is hoped that further research will examine other works and types of music in order to improve this model and make it useful to other music researchers. In addition to its benefits for performance analysis, it is suggested that the model has clear applications at least in music fraud detection, Music Information Retrieval (MIR) and in pedagogical applications for music education

Passive acoustic monitoring for assessment of natural and anthropogenic sound sources in the marine environment using automatic recognition

In the marine environment, sound can be an efficient source of information. Indeed, several marine species, including fish, use sound to navigate, select habitats, detect predators and prey, and to attract mates. Therefore, all the abiotic, biotic and manmade sounds that comprise the soundscape, have the potential to be used to assess and monitor species and marine environments. Passive acoustic monitoring (PAM) involves the use of acoustic sensors to record sound in the environment, from which relevant ecological information can be inferred. This thesis studied marine soundscapes, with special attention on fish communities, anthropogenic noise, and applied several methods to analyse acoustic recordings. Most of the focus was on the Tagus estuary, where the presence of two highly vocal species is known: the Lusitanian toadfish (Halobatrachus didactylus) and the meagre (Argyrosomus regius). Azorean and Mozambique soundscapes were also analysed. Several methods were applied to extract information and to visualize soundscape characteristics, including sound recognition systems based on hidden Markov models to recognize fish sounds and boat passages. Analysis of several types of marine environments and time scales showed several advantages and disadvantages of different methods. The use of sound pressure level on different frequency bands allowed the quantification of daily and seasonal patterns. Ecoacoustic indices appear to be cost-effective tools to monitor biodiversity in some marine environments. Using automatic recognition, vocal rhythms (diel and seasonal patterns) and vocal interactions among individuals were also characterized. Furthermore, boat noise effects on fish were studied: we encountered impacts on the audition, vocal behaviour and reproduction. Overall, we used PAM as a tool to remotely assess and monitor soundscapes, biodiversity, fish communities’ seasonal patterns, fish behaviour, species presence, and the effect of anthropogenic noise aiming to contribute for the management and conservation of marine ecosystems

Bayesian methods in music modelling

This thesis presents several hierarchical generative Bayesian models of musical signals designed to improve the accuracy of existing multiple pitch detection systems and other musical signal processing applications whilst remaining feasible for real-time computation. At the lowest level the signal is modelled as a set of overlapping sinusoidal basis functions. The parameters of these basis functions are built into a prior framework based on principles known from musical theory and the physics of musical instruments. The model of a musical note optionally includes phenomena such as frequency and amplitude modulations, damping, volume, timbre and inharmonicity. The occurrence of note onsets in a performance of a piece of music is controlled by an underlying tempo process and the alignment of the timings to the underlying score of the music. A variety of applications are presented for these models under differing inference constraints. Where full Bayesian inference is possible, reversible-jump Markov Chain Monte Carlo is employed to estimate the number of notes and partial frequency components in each frame of music. We also use approximate techniques such as model selection criteria and variational Bayes methods for inference in situations where computation time is limited or the amount of data to be processed is large. For the higher level score parameters, greedy search and conditional modes algorithms are found to be sufficiently accurate. We emphasize the links between the models and inference algorithms developed in this thesis with that in existing and parallel work, and demonstrate the effects of making modifications to these models both theoretically and by means of experimental results

Multikonferenz Wirtschaftsinformatik (MKWI) 2016: Technische Universität Ilmenau, 09. - 11. März 2016; Band I

Übersicht der Teilkonferenzen Band I: • 11. Konferenz Mobilität und Digitalisierung (MMS 2016) • Automated Process und Service Management • Business Intelligence, Analytics und Big Data • Computational Mobility, Transportation and Logistics • CSCW & Social Computing • Cyber-Physische Systeme und digitale Wertschöpfungsnetzwerke • Digitalisierung und Privacy • e-Commerce und e-Business • E-Government – Informations- und Kommunikationstechnologien im öffentlichen Sektor • E-Learning und Lern-Service-Engineering – Entwicklung, Einsatz und Evaluation technikgestützter Lehr-/Lernprozess

PASSIVE THERMAL CONTROL SYSTEMS FOR SPACE INSTRUMENTS MAKING – SCIENTIFIC BACKGROUND, QUALIFICATION, EXPLOITATION IN SPACE

Passive thermal control systems (TCS) are one of obligatory system of any space mission, used as on large spacecraft and microsatellites Supporting of required temperature range for space instruments is supported by rational design of TCS with optimal choice of main thermal control components such as multilayer insulation, optical coatings, heat conductive elements, heat insulation supports, thermal conductive gaskets, radiating surfaces and other elements. New ideology in TCS design has come after appearance of new element – heat pipe(s) which is a super heat conductive thermal conductor with constant or variable thermal properties

Effective Reservoir Management Using Streamline-Based Reservoir Simulation, History Matching and Rate Allocation Optimization

The use of the streamline-based method for reservoir management is receiving increased interest in recent years because of its computational advantages and intuitive appeal for reservoir simulation, history matching and rate allocation optimization. Streamline-based method uses snapshots of flow path of convective flow. Previous studies proved its applicability for convection dominated process such as waterflooding and tracer transport. However, for a case with gas injection with strong capillarity and gravity effects, the streamline-based method tends to lose its advantages for reservoir simulation and may result in loss of accuracy and applicability for history-matching and optimization problems. In this study, we first present the development of a 3D 3-phase black oil and compositional streamline simulator. Then, we introduce a novel approach to incorporate capillary and gravity effects via orthogonal projection method. The novel aspect of our approach is the ability to incorporate transverse effects into streamline simulation without adversely affecting its computational efficiency. We demonstrate our proposed method for various cases, including CO2 injection scenario. The streamline model is shown to be particularly effective to examine and visualize the interactions between heterogeneity which resulting impact on the vertical and areal sweep efficiencies. Next, we apply the streamline simulator to history matching and rate optimization problems. In the conventional approach of streamline-based history matching, the objective is to match flow rate history, assuming that reservoir energy was matched already, such as pressure distribution. The proposed approach incorporates pressure information as well as production flow rates, aiming that reservoir energy are also reproduced during production rate matching. Finally, we develop an NPV-based optimization method using streamline-based rate reallocation algorithm. The NPV is calculated along streamline and used to generate diagnostic plots of the effectiveness of wells. The rate is updated to maximize the field NPV. The proposed approach avoids the use of complex optimization tools. Instead, we emphasize the visual and the intuitive appeal of streamline methods and utilize flow diagnostic plots for optimal rate allocation. We concluded that our proposed approach of streamline-based simulation, inversion and optimization algorithm improves computational efficiency and accuracy of the solution, which leads to a highly effective reservoir management tool that satisfies industry demands