Giovanni De Poli

Representations of musical signals

Fourteen articles by different authors describe digital audio and computer music systems made possible by advances in digital signal processing theory, hardware design, and programming techniques. They focus on models that combine time-domain and frequency-domain representations (grains, wavelets,

Sonological models for timbre characterization

Abstract In the research on timbre, two important variables have to be assigned from the onset: the instruments used to analyze and to model the physical sound, and the techniques employed to provide an efficient and manageable representation of the data. The experimental methodology which results from these choices defines a sonological model; several different psychoacoustical and analytical tools have been employed to this aim in the past. In this paper we will present a series of experiments conducted at the CSC‐University of Padova, which attempted to define an experimental framework for the development of algorithmically defined timbre spaces. Fundamental to our line of research has been the use of analysis methods borrowed from the speech processing community and of data‐representation techniques such as neural networks and statistical tools. The results show several analogies to the classical timbre spaces defined in the literature; this has proved very important in order to explore the qualities ...

Auditory Modelling and Self-Organizing Neural Networks for Timbre Classification

Abstract A timbre classification system based on auditory processing and Kohonen self organizing neural networks is described. Preliminary results are given on a simple classification experiment involving 12 instruments in both clean and degraded conditions.

Note‐by‐note analysis of the influence of expressive intentions and musical structure in violin performance*

Abstract This paper describes an analysis of how the performance of a score differs when musicians are requested to play with differing expression. A professional violinist was asked to play short pieces of music in different versions expressing light, heavy, soft, hard, bright, and dark. For comparison, a normal, standard performance was recorded. Note‐by‐note analysis allowed us to identify the variations of the main acoustic parameters as a consequence of varying the expressive intentions. It was possible to identify two distinct expressive sources. The first refers the musical structure of the period, its division into phrases and the continuous alternation of tension and relaxation points. The second depends on the expressive intentions that the musician wants to convey to the listeners.

An Abstract Control Space for Communication of Sensory Expressive Intentions in Music Performance

Expressiveness is not an extravagance: instead, expressiveness plays a critical role in rational decision-making, in perception, in human interaction, in human emotions and in human intelligence. These facts, combined with the development of new informatics systems able to recognize and understand different kinds of signals, open new areas for research. A new model is suggested for computer understanding of sensory expressive intentions of a human performer and both theoretical and practical applications are described for human-computer interaction, perceptual information retrieval, creative arts and entertainment. Recent studies demonstrated that by opportunely modifying systematic deviations introduced by the musician it is possible to convey different sensitive contents, such as expressive intentions and/or emotions. We present an space, that can be used as a user interface. It represents, at an abstract level, the expressive content and the interaction between the performer and an expressive synthesizer.

Methodologies for Expressiveness Modelling of and for Music Performance

Expression is an important aspect of music performance. It is the added value of a performance, and is part of the reason that music is interesting to listen to and sounds alive. Moreover, understanding and modelling expressive content communication is important in many engineering applications. In human musical performance, acoustical or perceptual changes in sound are organized in a complex way by the performer in order to communicate musical content to the listener. The same piece of music can be performed trying to convey a specific interpretation of the score by adding mutable expressive intentions. The analysis of these systematic deviations has led to the formulation of several models that try to describe their structures, with the aim of explaining where, how and why a performer modifies, sometime in an unconscious way, what is indicated by the notation of the score. Modelling paradigms and problems are reviewed and issues for future research efforts are discussed.

Audio Morphing Different Expressive Intentions for Multimedia Systems

Web Extras: Sample audio files and view a demo of the audio authoring tool.Download Real Jukebox for listening to the mp3 filesSonatina in sol (by Beethoven) played neutral (without any expressive intentions)Expressive performance of Sonatina in sol generated by the model in a symbolic way (that is, as a MIDI file)Sonata K545 (by Mozart) played neutral (without any expressive intentions)Expressive performance of Sonata K545 generated by the model in a symbolic way (that is, as a MIDI file)Expressive performance of Sonata in A Major Op. V(by Corelli) generated by the audio authoring tool (using the audio postprocessing tool)

Physically informed signal processing methods for piano sound synthesis: a research overview

This paper reviews recent developments in physics-based synthesis of piano. The paper considers the main components of the instrument, that is, the hammer, the string, and the soundboard. Modeling techniques are discussed for each of these elements, together with implementation strategies. Attention is focused on numerical issues, and each implementation technique is described in light of its efficiency and accuracy properties. As the structured audio coding approach is gaining popularity, the authors argue that the physical modeling approach will have relevant applications in the field of multimedia communication.

A Tutorial on Digital Sound Synthesis Techniques

Progress in electronics and computer technology has led to an ever-increasing utilization of digital techniques for musical sound production. Some of these are the digital equivalents of techniques employed in analog synthesizers and in other fields of electrical engineering. Other techniques have been specifically developed for digital music devices and are peculiar to these. This paper introduces the fundamentals of the main digital synthesis techniques. Mathematical developments have been restricted in the exposition and can be found in the papers listed in the references. To simplify the discussion, whenever possible, the techniques are presented with reference to continuous signals. Sound synthesis is a procedure used to produce a sound without the help of acoustic instruments. In digital synthesis, a sound is represented by a sequence of numbers (samples). Hence, a digital synthesis technique consists of a computing procedure or mathematical formula, which computes each sample value. Normally, the synthesis formula depends on some values, that is, parameters. Frequency and amplitude are examples of such parameters. Parameters can be constant or slowly time variant during the sound. Time-variant parameters are also called control functions. Synthesis techniques can be classified as (1) generation techniques (Fig. la), which directly produce the signal from given data, and (2) transformation techniques (Fig. Ib), which can be divided into two stages, the generation of one or more simple signals and their modification. Often, more or less elaborate combinations of these techniques are employed. Fixed-Waveform Synthesis

Algorithms and Structures for Synthesis Using Physical Models

Sound synthesis by means of simulated physical models has gained popularity in the last few years. One of the principal reasons for this interest is that this technique, based on modeling the mechanism of production of sound, seems to offer the musician simpler tools for controlling and producing both new and traditional sonorities. In general the aim of any model is to describe the fundamental aspects of the phenomenon in question by means of mathematical relationships. Most often models are used for purposes of analysis. In science and engineering, models are commonly used for the purpose of understanding physical phenomena. This is especially true in musical acoustics, where it is common practice to study a traditional instrument through its physical model in order to understand how it works (Keefe 1992; Woodhouse 1992). In the pioneering work of Hiller and Ruiz (1971), physical models were used with the goal of producing musical sounds. Since that time, physical models have been used for synthesis purposes. In this article we examine how models can be constructed for musical applications and discuss the principles that inspire the most widely used synthesis algorithms. We will also try to compare physicalmodel-based and traditional synthesis methods by discussing their structural properties. For all structures and models discussed below there are some important general truths. First, a common way of approaching the problem of modeling physical systems is to describe their observed behavior in the frequency domain. Frequency models are particularly effective for the description of linear systems, but such systems rarely apply for musical instruments. When nonlinearities must be taken into account, modeling in the frequency domain often becomes unfeasible, especially when strong nonlinearities are involved. In this case, models in the time domain are preferable. Moreover, we know that any simulation requires the continuous-time model to be made discrete. This, of course, must be done in such a way as to reproduce with good approximation the behavior of the continuous-time model to which it refers.