Amílcar Cardoso

All the Truth About NEvAr

The use of Evolutionary Computation approaches to generate images has reached a great popularity. This led to the emergence of a new art form—Evolutionary Art—and to the proliferation of Evolutionary Art Tools. In this paper, we present an Evolutionary Art Tool, NEvAr, the experimental results achieved, and the work methodology used to generate images. In NEvAr, useful individuals are stored in a database in order to allow their reuse. This database is playing an increasingly important role in the creation of new images, which led us to the development of automatic seeding procedures, also described. The automation of fitness assignment is one of our present research interests. We will, therefore, describe some preliminary results achieved with our current approach to automatic evaluation.

Converging on the Divergent: The History (and Future) of the International Joint Workshops in Computational Creativity

We survey the history of studies of Computational Creativity, following the development of the International Conference on Computational Creativity from its beginnings, a decade ago, in two parallel workshop series. We give a brief outline of key issues, and a summary of the various different approaches taken by participants in the research field. The outlook is optimistic: a lot has been achieved in 10 years.

Melody Detection in Polyphonic Musical Signals: Exploiting Perceptual Rules, Note Salience, and Melodic Smoothness

80 Computer Music Journal Melody extraction from polyphonic audio is a research area of increasing interest. It has a wide range of applications in various fields, including music information retrieval (MIR, particularly in query-by-humming, where the user hums a tune to search a database of musical audio), automatic melody transcription, performance and expressiveness analysis, extraction of melodic descriptors for music content metadata, and plagiarism detection, to name but a few. This area has become increasingly relevant in recent years, as digital music archives are continuously expanding. The current state of affairs presents new challenges to music librarians and service providers regarding the organization of large-scale music databases and the development of meaningful methods of interaction and retrieval. In this article, we address the problem of melody detection in polyphonic audio following a multistage approach, inspired by principles from perceptual theory and musical practice. Our system comprises three main modules: pitch detection, determination of musical notes (with precise temporal boundaries, pitches, and intensity levels), and identification of melodic notes. The main contribution of this article is in the last module, in which a number of rule-based systems are proposed that attempt to extract the notes that convey the main melodic line among the whole set of detected notes. The system performs satisfactorily in a small database collected by us and in the database created for the ISMIR 2004 melody extraction contest. However, the performance of the algorithm decreased in the MIREX 2005 database. Related Work

Adaptive Critics for Evolutionary Artists

We focus on the development of artificial art critics. These systems analyze artworks, extracting relevant features, and produce an evaluation of the perceived pieces. The ability to perform aesthetic judgments is a desirable characteristic in an evolutionary artificial artist. As such, the inclusion of artificial art critics in these systems may improve their artistic abilities. We propose artificial art critics for the domains of music and visual arts, presenting a comprehensive set of experiments in author identification tasks. The experimental results show the viability and potential of our approach.

Exploration of Unknown Environments with Motivational Agents

This paper addresses the problem of exploring unknown, dynamic environments with motivational agents. The goal is the acquisition of a model of the environment including models of the entities that populate the environment. We describe the exploration strategy of both single and multiple agents. Each agent performs directed exploration using an action selection method based on the maximization of the intensity of positive feelings and minimization of negative ones. The exploration strategy for multiple agents relies on considering a team leader that integrates the maps and coordinates the actions of the members of the team. We present and discuss the results of an experiment conducted in simulated environments.

Partially interactive evolutionary artists

User fatigue is probably the most pressing problem in current Interactive Evolutionary Computation systems. To address it we propose the use of automatic seeding procedure, phenotype filters, and partial automation fitness assignment. We test this approaches in the visual arts domain. To further enhance interactive evolution applications in aesthetic domains, we propose the use of artificial art critics—systems that perform stylistic and aesthetic valuations of art—presenting experimental results.

On the Evolution of Evolutionary Algorithms

In this paper we discuss the evolution of several components of a traditional Evolutionary Algorithm, such as genotype to phenotype mappings and genetic operators, presenting a formalized description of how this can be attained. We then focus on the evolution of mapping functions, for which we present experimental results achieved with a meta-evolutionary scheme.

On the development of critics in evolutionary computation artists

One of the problems in the use of evolutionary computer systems in artistic tasks is the lack of artificial models of human critics. In this paper, based on the state of the art and on our previous related work, we propose a general architecture for an artificial art critic, and a strategy for the validation of this type of system. The architecture includes two modules: the analyser, which does a pre-processing of the artwork, extracting several measurements and characteristics; and the evaluator, which, based on the output of the analyser, classifies the artwork according to a certain criteria. The validation procedure consists of several stages, ranging from author and style discrimination to the integration of critic in a dynamic environment together with humans.

A musical system for emotional expression

The automatic control of emotional expression in music is a challenge that is far from being solved. This paper describes research conducted with the aim of developing a system with such capabilities. The system works with standard MIDI files and develops in two stages: the first offline, the second online. In the first stage, MIDI files are partitioned in segments with uniform emotional content. These are subjected to a process of features extraction, then classified according to emotional values of valence and arousal and stored in a music base. In the second stage, segments are selected and transformed according to the desired emotion and then arranged in song-like structures. The system is using a knowledge base, grounded on empirical results of works of Music Psychology that was refined with data obtained with questionnaires; we also plan to use data obtained with other methods of emotional recognition in a near future. For the experimental setups, we prepared web-based questionnaires with musical segments of different emotional content. Each subject classified each segment after listening to it, with values for valence and arousal. The modularity, adaptability and flexibility of our systems architecture make it applicable in various contexts like video-games, theater, films and healthcare contexts.

An auditory model based approach for melody detection in polyphonic musical recordings

We present a method for melody detection in polyphonic musical signals based on a model of the human auditory system. First, a set of pitch candidates is obtained for each frame, based on the output of an ear model and periodicity detection using correlograms. Trajectories of the most salient pitches are then constructed. Next, note candidates are obtained by trajectory segmentation (in terms of frequency and pitch salience variations). Too short, low-salience and harmonically-related notes are then eliminated. Finally, the melody is extracted by selecting the most important notes at each time, based on their pitch salience. We tested our method with excerpts from 12 songs encompassing several genres. In the songs where the solo stands out clearly, most of the melody notes were successfully detected. However, for songs where the melody is not that salient, the algorithm was not very accurate. Nevertheless, the followed approach seems promising.