Essen as a Corpus of Early Musical Experience

Statistics derived from the Essen Folksong Collection have widely been used as a proxy for general stylistic norms familiar to Western listeners. Since the specific facets of contemporary musical experience best modeled by a corpus of nineteenth-century European folksongs remain ambiguous, this study tests whether Essen-like music might be familiar to North American listeners through common children’s songs. Comparison with a corpus of 38 English-language children’s songs highly popular in North America finds that scale degrees from Essen and the children’s song corpus have near-perfect correlations in frequency profiles as well as high to very high correlations in tonal expectations and 4-grams. Profiles of scale degrees’ downbeat probabilities and average durations have moderate to high correlations for the diatonic but not the total chromatic. Overall, profiles of scale-degree behavior from the children’s song corpus match profiles from Essen more closely than do profiles from another corpus of music widely familiar to contemporary listeners (Billboard Hot 100 songs) and similarly closely as a corpus of nineteenth-century common-practice German vocal music (Schubert songs). For contemporary North American listeners, studies relying on Essen might plausibly be reinterpreted in terms of Essen acting as a corpus of early musical experience although the generalizability of Essen-derived statistics likely depends on the precise statistics being measured

Modulo7 : A Full Stack Music Information Retrieval and Structured Querying Engine

In this thesis, the author proposes and implements a new Music Information Retrieval and Structured Querying Engine called Modulo7. Unlike other MIR software which primarily deal with low level audio features \cite{musicrecSurvey}, Modulo7 operates at a higher abstraction level, on the principles of music theory and a symbolic representation of music(by treating musical notes instead of acoustic pitches as the basic blocks of representation of musical data). Modulo7 is implemented as a full stack deployment, with server components that parse various sources of music data into its own efficient internal representation and a client component that allows consumers to query the system with SQL like queries which satisfies certain music theory criteria (and as a consequence Modulo7 has a custom relational algebra with its basic building blocks based on music theory), along with a traditional search model based on non trivial similarity metrics for symbolic music. Modulo7 also implements a lyrics analyzer, which supports functions such as lyrics similarity and meta data prediction (e.g genre prediction)

Музикальний сервіс для аналізу аудіо

Дана робота розглядає та використовує алгоритми аналізу аудіо даних що складають основу розроблюваної підсистеми сервісу аналізу музичних творів. У ході роботи обрано технології обчислень характеристик музики, що несуть найбільше практичне значення та проаналізовано їх ефективність. У результаті розроблено модулі для обробки аудіо даних, що здійснюють обчислення двух типів спектрального представлення звуку, та знаходження ритму, пульсу та тональності музичного твору. Обчислені результати зберігаються для можливості проведення наступного етапу аналізу – прогнозування акордів аудіо. Розроблений сервіс дозволяє здійснити попередній аналіз музичного твору та виявити його ключові характеристики. Програмне забезпечення реалізовано мовою Python.This project researches and makes use of audio data analysis algorithms that will make a core of a system that implements a music piece analysis service. As a part of research the music parameters calculation technologies were considered and there practical value and efficiency were evaluated. The end result includes developing multiple modules for processing audio that implement calculating two different types of spectral form of the sound and finding the rhythm, beat and key of the musical piece. The results of the mentioned operations are stored for further analysis step – identifying the chords of a song. The service implemented in this project allows to perform the previous analysis of an audio and to find its key parametres. The software is implemented in Python programming language

Modelling the perception and composition of Western musical harmony.

PhD ThesisHarmony is a fundamental structuring principle in Western music, determining how simultaneously occurring musical notes combine to form chords, and how successions of chords combine to form chord progressions. Harmony is interesting to psychologists because it unites many core features of auditory perception and cognition, such as pitch perception, auditory scene analysis, and statistical learning. A current challenge is to formalise our psychological understanding of harmony through computational modelling. Here we detail computational studies of three core dimensions of harmony: consonance, harmonic expectation, and voice leading. These studies develop and evaluate computational models of the psychoacoustic and cognitive processes involved in harmony perception, and quantitatively model how these processes contribute to music composition. Through these studies we examine long-standing issues in music psychology, such as the relative contributions of roughness and harmonicity to consonance perception, the roles of low-level psychoacoustic and high-level cognitive processes in harmony perception, and the probabilistic nature of harmonic expectation. We also develop cognitively informed computational models that are capable of both analysing existing music and generating new music, with potential applications in computational creativity, music informatics, and music psychology. This thesis is accompanied by a collection of open-source software packages that implement the models developed and evaluated here, which we hope will support future research into the psychological foundations of musical harmony.