Background

This paper presents the most recent phase of a research project now in its fourth year. The project aims to define general principles for an analytical methodology of electronic music in its various forms, with a specific focus on perception and listening. In previous years, we explored different repertoires: from the analysis of electroacoustic compositions (2020/21) to works from the cultivated acousmatic repertoire (2021/22). We subsequently extended the research to the analysis of live performance, examining original compositions by students of the Department of New Languages at the Pescara Conservatory (2022/23). Last year, the focus shifted to the historical repertoire, specifically Musica su due dimensioni by Bruno Maderna (2023/24).In the most recent year, our work has concentrated on three compositions by Natasha Barrett: Growth (2022), Animalcules(2010), and Water Fall (2007). Barrett was chosen as one of the leading electroacoustic composers of the last quarter-century and for the clarity with which she articulated her aesthetic goals and communicative relationship with listeners, beginning with her doctoral thesis (1997).Throughout the various phases of the research and for each repertoire explored, we adopted a listening-centered perspective and conducted perceptual analysis using the analytical methodologies developed by Smalley [1997] and Hirst [2004]. In parallel, we implemented signal analysis using the MIR Toolbox software, with the aim of comparing data collected from different sources through a particularly articulated set of functions. 

Objectives and Methods

The current research aims to:

Understand to what extent, and through which concrete compositional strategies, Natasha Barrett has achieved her stated -- objectives, and how these objectives were perceived through the listening experience of a significant panel of listeners.

Highlight the fundamental paradigms through which compositional processes can be structured in order to establish an effective relationship between acoustic features and the listener's emotional and attentional responses.

 The results were obtained through a comparative analysis of:

Data collected through questionnaires administered after listening to three compositions presented to a panel of approximately 90 listeners, across four different locations and under homogeneous listening conditions (the Conservatories of Bari, Monopoli, Pesaro and Pescara).

Audio signal analysis conducted using MIR Toolbox.

A comparison with the composer's own statements, highlighted through Machine Learning (AI) algorithms.

Analytical examination of the compositions using Smalley's spectromorphological criteria and Hirst's S.I.A.M. model (Segregation, Integration, Assimilation, and Meaning).

Throughout the various phases of the research and for each repertoire explored, we adopted a listening-centered perspective and conducted perceptual analysis using the analytical methodologies developed by Smalley [1997] and Hirst [2004]. In parallel, we implemented signal analysis using the MIR Toolbox software, with the aim of comparing data collected from different sources through a particularly articulated set of functions. 

 Conclusions

 The results obtained through the process described above will subsequently be compared with those of previous studies in order to refine the tools and methodologies employed and to validate their effectiveness. Further elements will also be considered to enhance their reliability as an adaptive and flexible system supporting the analysis of electronic music.