Joaquín Mora

Spanish Family Quality of Life Scales: Under and over 18 years old

Abstract Background Researchers, professionals, and families have shown increasing concern with the family quality of life (FQoL) of people with intellectual disability (ID) and their families. The goals of this research were (a) to explore how Spanish families understand FQoL by developing 2 different measurement tools for families with a member with ID under and over 18 years old, and (b) to provide 2 diagnostic instruments that will be useful for designing action plans. Method The study comprised 4 stages: (a) focus groups, (b) expert assessment, (c) pilot study, and (d) normalisation and standardisation. The data were collected in 5 regions in Spain, and 1,205 families with a member with ID took part in the normalisation and standardisation of the scales. Results Both FQoL scales were consistent and have valid psychometric characteristics. Conclusions The scales have a diagnostic purpose for use in designing action plans aimed at producing significant changes in families’ lives.

Corpus COFLA: A Research Corpus for the Computational Study of Flamenco Music

Flamenco is a music tradition from Southern Spain that attracts a growing community of enthusiasts around the world. Its unique melodic and rhythmic elements, the typically spontaneous and improvised interpretation, and its diversity regarding styles make this still largely undocumented art form a particularly interesting material for musicological studies. In prior works, it has already been demonstrated that research on computational analysis of flamenco music, despite it being a relatively new field, can provide powerful tools for the discovery and diffusion of this genre. In this article, we present corpusCOFLA, a data framework for the development of such computational tools. The proposed collection of audio recordings and metadata serves as a pool for creating annotated subsets that can be used in development and evaluation of algorithms for specific music information retrieval tasks. First, we describe the design criteria for the corpus creation and then provide various examples of subsets drawn from the corpus. We showcase possible research applications in the context of computational study of flamenco music and give perspectives regarding further development of the corpus.

Melodic Contour and Mid-Level Global Features Applied to the Analysis of Flamenco Cantes

This work focuses on the topic of melodic characterization and similarity in a specific musical repertoire: a cappella flamenco singing, more specifically in debla and martinete styles. We propose the combination of manual and automatic description. First, we use a state-of-the-art automatic transcription method to account for general melodic similarity from music recordings. Second, we define a specific set of representative mid-level melodic features, which are manually labelled by flamenco experts. Both approaches are then contrasted and combined into a global similarity measure. This similarity measure is assessed by inspecting the clusters obtained through phylogenetic algorithms and by relating similarity to categorization in terms of style. Finally, we discuss the advantage of combining automatic and expert annotations as well as the need to include repertoire-specific descriptions for meaningful melodic characterization in traditional music collections.

Computational Ethnomusicology: A Study of Flamenco and Arab-Andalusian Vocal Music

In this chapter we approach flamenco and Arab-Andalusian vocal music through the analysis of two representative pieces. We apply a hybrid methodology consisting of audio-signal processing to describe and contrast their melodic characteristics followed by musicological analysis. The use of such computational analysis tools complements a musicological-historical study with the aim of supporting the discovery and understanding of the specific characteristics of these musical traditions, their similarities and differences, while offering solutions to more general music information retrieval (MIR) research challenges.