Tiago Fernandes Tavares

Survey on automatic transcription of music

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.

An interactive audio-visual installation using ubiquitous hardware and web-based software deployment

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. Subjects Human–Computer Interaction, Multimedia

Unsupervised note activity detection in NMF-based automatic transcription of piano music

Non-negative matrix factorization (NMF) is a traditionalsignal processing technique that allows approximating a measured spectrum as a weighted sum of known spectra. It may be applied to automatic transcription of piano music by assuming that each note is related to a known spectral template. In this model, active notes are related to peaks that can be identified by thresholding. We show that the optimal threshold is highly dependent on the timbre of the particular instrument used to play the analysed audio file. This can cause difficulties to the transcription. We propose an unsupervised technique that allows finding a threshold based on known properties of the audio signal. We show that the proposed technique’s performance compares favourably with manual threshold optimization. Moreover, we provide instructions and source codes that allow reproducing all described experiments.

Unsupervised training of detection threshold for polyphonic musical note tracking based on event periodicity

A common approach to the detection of simultaneous musical notes in an acoustic recording involves defining a function that yields activation levels for each candidate musical note over time. These levels tend to be high when the note is active and low when it is not. Therefore, by applying a simple threshold decision process, it is possible to decide whether each note is active or not at a given time. Such a threshold, in general, is hard to set and has no physical meaning. In this paper, it is shown that the rhythmic characteristic of the musical signal may be used to obtain a suitable threshold. The proposed method for obtaining the threshold is shown to have a greater generalization capability over different databases.

Personalizing self-organizing music spaces with anchors: design and evaluation

We propose and evaluate a system for content-based visualization and exploration of music collections. The system is based on a modification of Kohonen’s Self-Organizing Map algorithm and allows users to choose the locations of clusters containing acoustically similar tracks on the music space. A user study conducted to evaluate the system shows that the possibility of personalizing the music space was perceived as difficult. Conversely, the user study and objective metrics derived from users’ interactions with the interface demonstrate that the proposed system helped individuals create playlists faster and, under some circumstances, more effectively. We believe that personalized browsing interfaces are an important area of research in Multimedia Information Retrieval, and both the system and user study contribute to the growing work in this field.

Sonification of population behavior in particle swarm optimization

High-dimensional optimization problems may be addressed using populational meta-heuristics, whose statistical properties may indicate important characteristics of the optimization process. There is a great number of these properties, which means their joint visualization may become impractical. We developed a method for sonically displaying characteristics of population dynamics in particle swarm optimization processes as soundscape parameters. This process allows jointly analyzing several dimensions of the populations dynamics. Moreover, design decisions aimed at generating aesthetically appealing soundscapes, which allows the proposed system to be used as an automated music composition environment.