Identification of expressive descriptors for style extraction in music analysis using linear and nonlinear models

La formalización de las interpretaciones expresivas aún se considera relevante debido a la complejidad de la música. La interpretación expresiva forma un aspecto importante de la música, teniendo en cuenta diferentes convenciones como géneros o estilos que una interpretación puede desarrollar con el tiempo. Modelar la relación entre las expresiones musicales y los aspectos estructurales de la información acústica requiere una base probabilística y estadística mínima para la robustez, validación y reproducibilidad de aplicaciones computacionales. Por lo tanto, es necesaria una relación cohesiva y una justificación sobre los resultados. Esta tesis se sustenta en la teoría y aplicaciones de modelos discriminativos y generativos en el marco del aprendizaje de maquina y la relación de procedimientos sistemáticos con los conceptos de la musicología utilizando técnicas de procesamiento de señales y minería de datos. Los resultados se validaron mediante pruebas estadísticas y una experimentación no paramétrica con la implementación de un conjunto de métricas para medir aspectos acústicos y temporales de archivos de audio para entrenar un modelo discriminativo y mejorar el proceso de síntesis de un modelo neuronal profundo. Adicionalmente, el modelo implementado presenta la oportunidad para la aplicación de procedimientos sistemáticos, automatización de transcripciones usando notación musical, entrenamiento de habilidades auditivas para estudiantes de música y mejorar la implementación de redes neuronales profundas usando CPU en lugar de GPU debido a las ventajas de las redes convolucionales para el procesamiento de archivos de audio como vectores o matriz con una secuencia de notas.MaestríaMagister en Ingeniería Electrónic

Automatic music transcription: challenges and future directions

Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects

Automated Analysis of Synchronization in Human Full-body Expressive Movement

The research presented in this thesis is focused on the creation of computational models for the study of human full-body movement in order to investigate human behavior and non-verbal communication. In particular, the research concerns the analysis of synchronization of expressive movements and gestures. Synchronization can be computed both on a single user (intra-personal), e.g., to measure the degree of coordination between the joints\u2019 velocities of a dancer, and on multiple users (inter-personal), e.g., to detect the level of coordination between multiple users in a group. The thesis, through a set of experiments and results, contributes to the investigation of both intra-personal and inter-personal synchronization applied to support the study of movement expressivity, and improve the state-of-art of the available methods by presenting a new algorithm to perform the analysis of synchronization

ATEPP: A DATASET OF AUTOMATICALLY TRANSCRIBED EXPRESSIVE PIANO PERFORMANCE

Computational models of expressive piano performance rely on attributes like tempo, timing, dynamics and pedalling. Despite some promising models for performance assessment and performance rendering, results are limited by the scale, breadth and uniformity of existing datasets. In this paper, we present ATEPP, a dataset that contains 1000 hours of performances of standard piano repertoire by 49 world-renowned pianists, organized and aligned by compositions and movements for comparative studies. Scores in MusicXML format are also available for around half of the tracks. We first evaluate and verify the use of transcribed MIDI for representing expressive performance with a listening evaluation that involves recent transcription models. Then, the process of sourcing and curating the dataset is outlined, including composition entity resolution and a pipeline for audio matching and solo filtering. Finally, we conduct baseline experiments for performer identification and performance rendering on our datasets, demonstrating its potential in generalizing expressive features of individual performing style

Computational Models of Expressive Music Performance: A Comprehensive and Critical Review

Expressive performance is an indispensable part of music making. When playing a piece, expert performers shape various parameters (tempo, timing, dynamics, intonation, articulation, etc.) in ways that are not prescribed by the notated score, in this way producing an expressive rendition that brings out dramatic, affective, and emotional qualities that may engage and affect the listeners. Given the central importance of this skill for many kinds of music, expressive performance has become an important research topic for disciplines like musicology, music psychology, etc. This paper focuses on a specific thread of research: work on computational music performance models. Computational models are attempts at codifying hypotheses about expressive performance in terms of mathematical formulas or computer programs, so that they can be evaluated in systematic and quantitative ways. Such models can serve at least two purposes: they permit us to systematically study certain hypotheses regarding performance; and they can be used as tools to generate automated or semi-automated performances, in artistic or educational contexts. The present article presents an up-to-date overview of the state of the art in this domain. We explore recent trends in the field, such as a strong focus on data-driven (machine learning) approaches; a growing interest in interactive expressive systems, such as conductor simulators and automatic accompaniment systems; and an increased interest in exploring cognitively plausible features and models. We provide an in-depth discussion of several important design choices in such computer models, and discuss a crucial (and still largely unsolved) problem that is hindering systematic progress: the question of how to evaluate such models in scientifically and musically meaningful ways. From all this, we finally derive some research directions that should be pursued with priority, in order to advance the field and our understanding of expressive music performance

Affective calibration of musical feature sets in an emotionally intelligent music composition system

Affectively driven algorithmic composition (AAC) is a rapidly growing field that exploits computer-aided composition in order to generate new music with particular emotional qualities or affective intentions. An AAC system was devised in order to generate a stimulus set covering nine discrete sectors of a two-dimensional emotion space by means of a 16-channel feed-forward artificial neural network. This system was used to generate a stimulus set of short pieces of music, which were rendered using a sampled piano timbre and evaluated by a group of experienced listeners who ascribed a two-dimensional valence-arousal coordinate to each stimulus. The underlying musical feature set, initially drawn from the literature, was subsequently adjusted by amplifying or attenuating the quantity of each feature in order to maximize the spread of stimuli in the valence-arousal space before a second listener evaluation was conducted. This process was repeated a third time in order to maximize the spread of valence-arousal coordinates ascribed to the generated stimulus set in comparison to a spread taken from an existing prerated database of stimuli, demonstrating that this prototype AAC system is capable of creating short sequences of music with a slight improvement on the range of emotion found in a stimulus set comprised of real-world, traditionally composed musical excerpts

A Weighted Individual Performance-Based Assessment for Middle School Orchestral Strings: Establishing Validity and Reliability

The study established the validity and reliability of a weighted individual performance-based assessment tool within the utility scope of middle school orchestral strings. The following research questions guided this study: 1. What specific string-playing behaviors and corresponding criteria validate a weighted individual performance-based assessment tool for middle school orchestral strings? 2. What are the psychometric properties of the weighted individual performance-based assessment tool in authentic situations? For Research Question 1, the expert panel and I were able to 100% mutually agree on 10 string-playing behaviors: tempo, rhythm, tone, pitch, intonation, technique, bowing, dynamics, phrasing, and posture that created the DISAT. Being interdependent, these string-playing behaviors are relevant because they encompass every necessary facet of orchestral string performance (Zdzinski & Barnes, 2002). According to Zdzinski and Barnes (2002), an orchestral string performance assessment must evaluate each facet of a participant’s playing ability to rate the overall musicianship. Bergee and Rossin (2019) stated in their research that it is important to have various aspects of a performance utilized in a musical assessment. The DISAT obtained reliability of 0.872 by having enough variance between raters in the authentic situation. Linacre (2015) stated that reliability greater than 0.8 is acceptable to v distinguish separation between raters. Combined with the expert panel\u27s 100% mutual agreement on content validity, this proved the DISAT to be a valid and reliable assessment tool for individual performance-based orchestral strings assessment (AERA, APA, & NCME, 2014). The DISAT can be utilized by districts and middle school orchestral string music teachers in North Carolina. Being a consistent, objective tool, the DISAT can standardize our approach to middle school orchestral string music education assessment (AERA, APA, & NCME, 2014). The data collected by the DISAT could easily track the musical progression of students while giving opportunities for constructive, purposeful feedback