This paper describes an interactive audio-visual musical installation, namely MOTUS, that aims at being deployed using low-cost hardware and software. This was achieved by writing the software as a web application and using only hardware pieces that are built-in most modern personal computers. This scenario implies in specific technical restrictions, which leads to solutions combining both technical and artistic aspects of the installation. The resulting system is versatile and can be freely used from any computer with Internet access. Spontaneous feedback from the audience has shown that the provided experience is interesting and engaging, regardless of the use of minimal hardware.

Bird song identification has mainly been approached as a closed-set classification problem; that is, all samples are known to be from one of the classes known by the classifier. However, wildlife monitoring using bird songs is closer to an open-set classification setting, as the classifier is required to predict if a sample comes from an unknown origin, like an environmental sound or an unrelated animal. Furthermore, current approaches to bird song classification assume that the model can access the whole dataset and build optimal projections. This is not a realistic scenario in Brazil as the country has thousands of species, and it is unfeasible to build a dataset containing a representative diversity of samples of all of them. This work analyzes algorithms that can be used for the open-set classification of bird songs. The analyzed algorithms can fit models using data from one or from only a few species. The investigation revealed many current technical difficulties and highlighted several opportunities for future work in this field.

The feeling of immersion is an important aspect of gaming experiences. It can be greatly impacted by back- ground music. In this work, we investigate the use of algorithmically-generated background music as a mean to ge- nerate immersion in gaming experiences. For such, we developed two versions of the same game. One of them uses music written by a composer. The other uses real-time generated melodies based on a Markov chain. We evaluated the immersion level related to each of these versions using user questionnaires and performance measures. The re- sults did show only a small immersion difference between the versions. This indicates that algorithmic music can be a suitable option for game content generation.

Ethnic music collections provide vantage points into the culture of different communities. However, tools that allow the exploration of those music libraries by the general public are lacking. We present and evaluate an ethnic music collection exploration system that comprises a recommendation algorithm and a web-radio interface. The proposed system provides recommendations that adapt to the user’s preferences. A blind a/b user study conducted to evaluate the system suggests that personalized recommendations could help individuals from diverse backgrounds explore ethnic music collections. We believe both the system and its evaluation contribute to the growing work in the field of intelligent information systems.

An automatic music transcriber is a device that detects, without human interference, the musical gestures required to play a particular piece. Many techniques have been proposed to solve the problem of automatic music transcription. This paper presents an overview on the theme, discussing digital signal processing techniques, pattern classification techniques and heuristic assumptions derived from music knowledge that were used to build some of the main systems found in the literature. The paper is focused on the motivations behind each technique, aiming to serve both as an introduction to the theme and as resource for the development of new solutions for automatic transcription.