Mariana Montiel

Classical Recurrent Neural Networks (RNNs) summarize musical context into a deterministic hidden state vector, imposing an information bottleneck that fails to capture the inherent ambiguity in music. We propose the Density Matrix RNN (DM-RNN), a novel theoretical architecture utilizing the Density Matrix. This allows the model to maintain a statistical ensemble of musical interpretations (a mixed state), capturing both classical probabilities and quantum coherences. We rigorously define the temporal dynamics using Quantum Channels (CPTP maps). Crucially, we detail a parameterization strategy based on the Choi-Jamiolkowski isomorphism, ensuring the learned dynamics remain physically valid (CPTP) by construction. We introduce an analytical framework using Von Neumann Entropy to quantify musical uncertainty and Quantum Mutual Information (QMI) to measure entanglement between voices. The DM-RNN provides a mathematically rigorous framework for modeling complex, ambiguous musical structures.

The mathematical music composition workshop, an endeavor that integrates mathematics and music majors, is a concrete example of a STEAM (science, technology, engineering, arts, and mathematics) project. In this article, the authors analyze how mathematics students and music composition students have worked together, have learned to interpret specialized languages from one another, and have presented their results to a public interested in the relationship that guards these two disciplines. The goal of improving the understanding of abstract mathematical concepts through the application to musical structures is analyzed using the Onto-semiotic Approach (OSA). This framework sheds light on some of the written and oral manifestations of the students who participated. The mathematical competence that the participants were expected to achieve through this interdisciplinary endeavor are privileged in this article over the music education goals (which were also present).

In this review, we summarize the key topics discussed at the Mathematics and Computation in Music (MCM) conference held in Atlanta from June 21–24, 2022. MCM is the flagship conference of the Society for Mathematics and Computation in Music. The subjects of the presentations included combinatorics and graph theory in scales and rhythm, categorical and algebraic approaches to music, algorithms and modelling for music and music-related phenomena, among many others that will be described. GRAPHICAL ABSTRACT

Maria Mannone was not given credit for the cover logo in the original version. This was corrected.The original version of chapter 14, “Distant Neighbors and Interscalar Contiguities” was revised. This chapter was previously published non-open access. It was changed to open access retrospectively under a CC BY 4.0 license. The book has also been updated with the change.

En el presente trabajo se presenta un reporte y un análisis de una experiencia interdisciplinaria que se llevó a cabo a través de un programa especial de fomento a modelos de esta naturaleza. La propuesta de colaboración entre los cursos Análisis Postonal de la Escuela de Música de la Universidad Estatal de Georgia (GSU) en los Estados Unidos y Teoría Matemática de la Música del Departamento de Matemáticas y Estadística de la misma universidad, fue seleccionada en un concurso sobre el tema de enseñanza interdisciplinaria integrada por parejas. Este artículo consiste en una revisión de algunos antecedentes documentados de esta combinación particular a nivel universitario, una descripción de los contenidos de los dos cursos y de los participantes en este modelo piloto, así como en un análisis de datos recopilados al final del curso. Una entrevista colectiva fue videograbada y el análisis de la información en general fue llevado a cabo de acuerdo con un marco conceptual adaptado a esta combinación novedosa de disciplinas. Se buscó en todo momento que el análisis y las conclusiones se circunscribiesen a parámetros concretos para poder detectar, si fuera el caso, las características precisas de la colaboración que contribuyeran al realce de los procesos de aprendizaje y enseñanza en ambas disciplinas.