Automatic Semantic Annotation of Music with Harmonic Structure

This paper presents an annotation model for harmonic structure of a piece of music, and a rule system that supports the automatic generation of harmonic annotations. Musical structure has so far received relatively little attention in the context of musical metadata and annotation, although it is highly relevant for musicians, musicologists and indirectly for music listeners. Activities in semantic annotation of music have so far mostly concentrated on features derived from audio data and file-level metadata. We have implemented a model and rule system for harmonic annotation as a starting point for semantic annotation of musical structure. Our model is for the musical style of Jazz, but the approach is not restricted to this style. The rule system describes a grammar that allows the fully automatic creation of an harmonic analysis as tree-structured annotations. We present a prototype ontology that defines the layers of harmonic analysis from chords symbols to the level of a complete piece. The annotation can be made on music in various formats, provided there is a way of addressing either chords or time points within the music. We argue that this approach, in connection with manual annotation, can support a number of application scenarios in music production, education, and retrieval and in musicology

The AFLOW Fleet for Materials Discovery

The traditional paradigm for materials discovery has been recently expanded to incorporate substantial data driven research. With the intent to accelerate the development and the deployment of new technologies, the AFLOW Fleet for computational materials design automates high-throughput first principles calculations, and provides tools for data verification and dissemination for a broad community of users. AFLOW incorporates different computational modules to robustly determine thermodynamic stability, electronic band structures, vibrational dispersions, thermo-mechanical properties and more. The AFLOW data repository is publicly accessible online at aflow.org, with more than 1.7 million materials entries and a panoply of queryable computed properties. Tools to programmatically search and process the data, as well as to perform online machine learning predictions, are also available.Comment: 14 pages, 8 figure

Automatic estimation of harmonic tension by distributed representation of chords

The buildup and release of a sense of tension is one of the most essential aspects of the process of listening to music. A veridical computational model of perceived musical tension would be an important ingredient for many music informatics applications. The present paper presents a new approach to modelling harmonic tension based on a distributed representation of chords. The starting hypothesis is that harmonic tension as perceived by human listeners is related, among other things, to the expectedness of harmonic units (chords) in their local harmonic context. We train a word2vec-type neural network to learn a vector space that captures contextual similarity and expectedness, and define a quantitative measure of harmonic tension on top of this. To assess the veridicality of the model, we compare its outputs on a number of well-defined chord classes and cadential contexts to results from pertinent empirical studies in music psychology. Statistical analysis shows that the model's predictions conform very well with empirical evidence obtained from human listeners.Comment: 12 pages, 4 figures. To appear in Proceedings of the 13th International Symposium on Computer Music Multidisciplinary Research (CMMR), Porto, Portuga

Frequency-Domain Analysis of Linear Time-Periodic Systems

In this paper, we study convergence of truncated representations of the frequency-response operator of a linear time-periodic system. The frequency-response operator is frequently called the harmonic transfer function. We introduce the concepts of input, output, and skew roll-off. These concepts are related to the decay rates of elements in the harmonic transfer function. A system with high input and output roll-off may be well approximated by a low-dimensional matrix function. A system with high skew roll-off may be represented by an operator with only few diagonals. Furthermore, the roll-off rates are shown to be determined by certain properties of Taylor and Fourier expansions of the periodic systems. Finally, we clarify the connections between the different methods for computing the harmonic transfer function that are suggested in the literature

Automated computation of materials properties

Materials informatics offers a promising pathway towards rational materials design, replacing the current trial-and-error approach and accelerating the development of new functional materials. Through the use of sophisticated data analysis techniques, underlying property trends can be identified, facilitating the formulation of new design rules. Such methods require large sets of consistently generated, programmatically accessible materials data. Computational materials design frameworks using standardized parameter sets are the ideal tools for producing such data. This work reviews the state-of-the-art in computational materials design, with a focus on these automated $\textit{ab-initio}$ frameworks. Features such as structural prototyping and automated error correction that enable rapid generation of large datasets are discussed, and the way in which integrated workflows can simplify the calculation of complex properties, such as thermal conductivity and mechanical stability, is demonstrated. The organization of large datasets composed of $\textit{ab-initio}$ calculations, and the tools that render them programmatically accessible for use in statistical learning applications, are also described. Finally, recent advances in leveraging existing data to predict novel functional materials, such as entropy stabilized ceramics, bulk metallic glasses, thermoelectrics, superalloys, and magnets, are surveyed.Comment: 25 pages, 7 figures, chapter in a boo

An Image Morphing Technique Based on Optimal Mass Preserving Mapping

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2007.896637Image morphing, or image interpolation in the time domain, deals with the metamorphosis of one image into another. In this paper, a new class of image morphing algorithms is proposed based on the theory of optimal mass transport. The 2 mass moving energy functional is modified by adding an intensity penalizing term, in order to reduce the undesired double exposure effect. It is an intensity-based approach and, thus, is parameter free. The optimal warping function is computed using an iterative gradient descent approach. This proposed morphing method is also extended to doubly connected domains using a harmonic parameterization technique, along with finite-element methods

Formulating representative features with respect to document genre classification

Genre classification (e.g. whether a document is a scientific article or magazine article) is closely bound to the physical and conceptual structure of document as well as the level of depth involved in the text. Hence, it provides a means of ranking documents retrieved by search tools according to metrics other than topical similarity. Moreover, the structural information derived from genre classification can be used to locate target information within the text. In previous studies, the detection of genre classes has been attempted by using some normalised frequency of terms or combinations of terms in the document (here, we are using term as a reference to words, phrases, syntactic units, sentences and paragraphs, as well as other patterns derived from deeper linguistic or semantic analysis). These approaches largely neglect how the term is distributed throughout the document. Here, we report the results of automated experiments based on distributive statistics of words in order to present evidence that term distribution pattern is a better indicator of genre class than term frequency.