Maths, Computation and Flamenco: overview and challenges

Flamenco is a rich performance-oriented art music genre from Southern Spain which attracts a growing community of aficionados around the globe. Due to its improvisational and expressive nature, its unique musical characteristics, and the fact that the genre is largely undocumented, flamenco poses a number of interesting mathematical and computational challenges. Most existing approaches in Musical Information Retrieval (MIR) were developed in the context of popular or classical music and do often not generalize well to non-Western music traditions, in particular when the underlying music theoretical assumptions do not hold for these genres. Over the recent decade, a number of computational problems related to the automatic analysis of flamenco music have been defined and several methods addressing a variety of musical aspects have been proposed. This paper provides an overview of the challenges which arise in the context of computational analysis of flamenco music and outlines an overview of existing approaches

EL canto (cante) al Cristo de la Cárcel en Mairena del Alcor

El presente estudio constituye un análisis multidisciplinar enfocado en un canto litúrgico (“Santo Dios”) que se interpreta en un contexto socio-religioso y que en la localidad de Mairena del Alcor ha evolucionado hacia una forma flamenca. Desde un punto de vista musical, exploramos las distintas versiones localizando los elementos ornamentales que permiten hablar de cambio formal. Por otro lado, desde la Teoría del Performance, analizamos el canto del Santo Dios como elemento integrado en un marco cultural que posibilita la emergencia e institucionalización de una nueva forma sonora, asumida por parte del colectivo como rasgo identitario. El objetivo que perseguimos con todo ello es mostrar la evolución de un fenómeno musical que puede servir como modelo interpretativo aplicable al estudio del origen y evolución de los estilos flamencos.This paper address a multidisciplinary analysis of a chant (“Santo Dios”) that takes place in a socialreligious context of Mairena del Alcor (Seville) where it has evolved into a flamenco form. First, from a musical point of view, we explore several versions and find the ornaments representing the formal change to the flamenco form. Second, from the performance theory point of view, we analyze the chant as an integrated element into a cultural framework that allows for the emergence and institutionalization of a new sound. In addition, this “way of doing” is assumed as a collective identity trait in Mairena del Alcor. With this, the aim is to show the evolution of a musical phenomena as a model for the study of the origin and evolution of the flamenco styles

Detection of Melodic Patterns in Automatic Transcriptions of Flamenco Singing

The spontaneous expressive interpretation of melodic templates is a fundamental concept in flamenco music. Consequently, the automatic detection of such patterns in music collections sets the basis for a number of challenging analysis and retrieval tasks. We present a novel algorithm for the automatic detection of manually defined melodies within a corpus of automatic transcriptions of flamenco recordings. We evaluate the performance on the example of five characteristic patterns from the fandango de Valverde style and demonstrate that the algorithm is capable of retrieving ornamented instances of query patterns. Furthermore, we discuss limitations, possible extensions and applications of the proposed system

On finding widest empty curved corridors

Open archive-ElsevierAn α-siphon of width w is the locus of points in the plane that are at the same distance w from a 1-corner polygonal chain C such that α is the interior angle of C. Given a set P of n points in the plane and a fixed angle α, we want to compute the widest empty α-siphon that splits P into two non-empty sets.We present an efficient O(n log3 n)-time algorithm for computing the widest oriented α-siphon through P such that the orientation of a half-line of C is known.We also propose an O(n3 log2 n)-time algorithm for the widest arbitrarily-oriented version and an (nlog n)-time algorithm for the widest arbitrarily-oriented α-siphon anchored at a given point

Detecting broken Absorber Tubes in CSP plants using intelligent sampling and dual loss

Concentrated solar power (CSP) is one of the growing technologies that is leading the process of changing from fossil fuels to renewable energies. The sophistication and size of the systems require an increase in maintenance tasks to ensure reliability, availability, maintainability and safety. Currently, automatic fault detection in CSP plants using Parabolic Trough Collector systems evidences two main drawbacks: 1) the devices in use needs to be manually placed near the receiver tube, 2) the Machine Learning-based solutions are not tested in real plants. We address both gaps by combining the data extracted with the use of an Unmaned Aerial Vehicle, and the data provided by sensors placed within 7 real plants. The resulting dataset is the first one of this type and can help to standardize research activities for the problem of fault detection in this type of plants. Our work proposes supervised machine-learning algorithms for detecting broken envelopes of the absorber tubes in CSP plants. The proposed solution takes the class imbalance problem into account, boosting the accuracy of the algorithms for the minority class without harming the overall performance of the models. For a Deep Residual Network, we solve an imbalance and a balance problem at the same time, which increases by 5% the Recall of the minority class with no harm to the F1-score. Additionally, the Random Under Sampling technique boost the performance of traditional Machine Learning models, being the Histogram Gradient Boost Classifier the algorithm with the highest increase (3%) in the F1-Score. To the best of our knowledge, this paper is the first providing an automated solution to this problem using data from operating plants