Artefact, Participant and Interaction in Auditory Experiences

Presented at the 27th International Conference on Auditory Display (ICAD 2022) 24-27 June 2022, Virtual conference.The act of sound perception and its subjective dimensions, from physical to psychoacoustics, from semantic to affective, carry an inherent challenge for the conception and evaluation of every audio-based artefact. Starting from a previous framework of evaluation approaches, we seek to deconstruct the configuring elements of these processes, searching for theoretical foundations informing Sound Design and possible applications for Auditory Displays. This work is a first step into identifying a body of knowledge on the listener’s experience, how the act of listening takes place and how the sequence of listening actions can evolve as forms of dialogue, creating dialogical spaces for making sense of auditory information. With this work, practitioners can gain new insights into how existing techniques for creating auditory artefacts can be configured and transformed into new, alternative approaches

User-Guided System to Generate Spanish Popular Music

[EN]The automatic generation of music is an emerging field of research that has attracted wide attention in Computer Science. Additionally, the interaction between users and machines is nowadays very present in our daily lives, and influences fields such as Economy, Sports or Arts. Following this approach, this work develops an intelligent system that generates melodies based on Spanish popular music and some indications of the users through an interface. The system creates a melody by learning from the corpus selected through a Markov model, which is also influenced by the users’ preferences. Several experiments were carried out to evaluate the musical quality and the usefulness of the system to interact with the user and generate music. The results of the evaluation shows that the proposal is able to generate music adapted to the style standards of Spanish popular music and to the users’ indications

O direito à informação e o (ainda restrito) espaço cidadão no Jornalismo Popular impresso

Este artigo objetiva mostrar como na era da informação o Jornalismo, muitas vezes, negligencia o estímulo ao exercício da cidadania, em especial o Jornalismo realizado pelos meios de Comunicação de grande circulação. Estes veículos, em geral, não priorizam temas que colaboram para a formação e o espírito crítico. A análise será focada nos jornais ditos populares, em especial o Super Notícia, periódico fundado em 2002, que se tornou o veículo impresso mais vendido no país, mas com circulação restrita aos municípios mineiros. A pertinência do assunto se dá já que os periódicos populares apresentam grande circulação e, muitas vezes, são a única fonte de leitura das chamadas classes subalternas. Para o estudo, utilizamos a Análise de Conteúdo. Entendemos que o espaço para o Jornalismo Comunitário é restrito, enquanto há ênfase em assuntos relacionados com o crime, os astros de TV e o futebol. No entanto, é preciso buscar este espaço e não apenas ficar no campo da crítica

Tra-la-lyrics: an approach to generate text based on rhythm

This paper is about an ongoing project, Tra-la-Lyrics which aims to create a computer program capable of gen- erating lyrics for given melodies. As a preliminary phase, the correlations between words and rhythm in a song’s lyrics was studied and some heuristics were achieved. Al- gorithms for syllabic division and syllabic stress identifi- cation on a word were implemented. A method for cal- culating the strength of each beat of a melody was also used. The actual system’s architecture is described. To get the contents of the lyrics we’ve used a relational database where we could find not only the words, but also their grammatical category and some morphological related at- tributes. Some ongoing work is also referred and some examples of the currently possible outputs of the system are presented. Conclusions and possible further work are finally discussed.CISU

A systemic perspective for sonification aesthetics

Presented at the 26th International Conference on Auditory Display (ICAD 2021) 25-28 June 2021, Virtual conference.For more than twenty-five years, the sonification field has been attempting to establish itself as a primary body of knowledge communicating through sound. Despite multiple efforts to embrace the interdisciplinary nature of the field and the subjective nature of sound, we wonder: is the tendency for dealing with such challenges through an objective, functional communication, with a single interpretation criterion, limiting the epistemic boundaries of action? How can a subjectively perceived medium such as sound be embraced in all its aesthetic dimensions? We propose a conceptual transition through the reframing of a sonification as a living system for creating aesthetic experiences. This will be achieved by drawing notions from phenomenology, embodied perception, human-computer interaction and soundscape theory. A systemic sonification distinguishes itself as an ever-evolving system built on dynamic structures that actively responds to changes in its environment and interactions from surrounding beings. Driven by a series of emerging concepts of non-linearity, networks, nested systems and intertwined relationships, the system's resilience and adaptability grows with each interaction, recentring the human protagonist as the weaver of his/her aesthetic experience through a selftranscendent process that expands the perception field

Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts

The term concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. Concepts are also studied in the computational domain through their symbolic, distributed and hybrid representations. The majority of these approaches focused on addressing concrete concepts notion, but the view of the abstract concept is rarely explored. Moreover, most computational approaches have a predefined structure or configurations. The proposed method, Regulated Activation Network (RAN), has an evolving topology and learns representations of abstract concepts by exploiting the geometrical view of concepts, without supervision. In the article, first, a Toy-data problem was used to demonstrate the RANs modeling. Secondly, we demonstrate the liberty of concept identifier choice in RANs modeling and deep hierarchy generation using the IRIS dataset. Thirdly, data from the IoT’s human activity recognition problem is used to show automatic identification of alike classes as abstract concepts. The evaluation of RAN with eight UCI benchmarks and the comparisons with five Machine Learning models establishes the RANs credibility as a classifier. The classification operation also proved the RANs hypothesis of abstract concept representation. The experiments demonstrate the RANs ability to simulate psychological processes (like concept creation and learning) and carry out effective classification irrespective of training data size

Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts

The term concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. Concepts are also studied in the computational domain through their symbolic, distributed and hybrid representations. The majority of these approaches focused on addressing concrete concepts notion, but the view of the abstract concept is rarely explored. Moreover, most computational approaches have a predefined structure or configurations. The proposed method, Regulated Activation Network (RAN), has an evolving topology and learns representations of abstract concepts by exploiting the geometrical view of concepts, without supervision. In the article, first, a Toy-data problem was used to demonstrate the RANs modeling. Secondly, we demonstrate the liberty of concept identifier choice in RANs modeling and deep hierarchy generation using the IRIS dataset. Thirdly, data from the IoT’s human activity recognition problem is used to show automatic identification of alike classes as abstract concepts. The evaluation of RAN with eight UCI benchmarks and the comparisons with fiveMachine Learning models establishes the RANs credibility as a classifier. The classification operation also proved the RANs hypothesis of abstract concept representation. The experiments demonstrate the RANs ability to simulate psychological processes (like concept creation and learning) and carry out effective classification irrespective of training data size