Time domain analysis/resynthesis of musical tones based on polynomial interpolation techniques

The 3rd International Conference on Signal Processing, Beijing, China, 14-18 October 1996In this paper, a novel algorithm employing polynomial interpolation techniques is proposed for the analysis and resynthesis of musical tones, based on time domain information. This algorithm models a single period of oscillation as a series of features, with curves joining such features together. The trajectories of these features and the shape of the curves across the whole input signal can be parameterised, such that the signal can be analysed and resynthesised as a close approximation to the original. The current research introduced an alternative approach to analyse/resynthesise sampled musical signals in the frequency domain, one which characterises a signal by its physical structure rather than its frequency components. The results from this analysis can be used to further refine existing physical models of musical instruments.published_or_final_versio

Comparison of Signal Reconstruction Methods for the Azimuth Discrimination and Resynthesis Algorithm

The Azimuth Discrimination and Resynthesis algorithm, (ADRess), has been shown to produce high quality sound source separation results for intensity panned stereo recordings. There are however, artifacts such as phasiness which become apparent in the separated signals under certain conditions. This is largely due to the fact that only the magnitude spectra for the separated sources are estimated. Each source is then resynthesised using the phase information obtained from the original mixture. This paper describes the nature and origin of the associated artifacts and proposes alternative techniques for resynthesising the separated signals. A comparison of each technique is then presented

Real-time Sound Source Separation For Music Applications

Sound source separation refers to the task of extracting individual sound sources from some number of mixtures of those sound sources. In this thesis, a novel sound source separation algorithm for musical applications is presented. It leverages the fact that the vast majority of commercially recorded music since the 1950s has been mixed down for two channel reproduction, more commonly known as stereo. The algorithm presented in Chapter 3 in this thesis requires no prior knowledge or learning and performs the task of separation based purely on azimuth discrimination within the stereo field. The algorithm exploits the use of the pan pot as a means to achieve image localisation within stereophonic recordings. As such, only an interaural intensity difference exists between left and right channels for a single source. We use gain scaling and phase cancellation techniques to expose frequency dependent nulls across the azimuth domain, from which source separation and resynthesis is carried out. The algorithm is demonstrated to be state of the art in the field of sound source separation but also to be a useful pre-process to other tasks such as music segmentation and surround sound upmixing

All the Noises:Hijacking Listening Machines for Performative Research

Research into machine listening has intensified in recent years creating a variety of techniques for recognising musical features suitable, for example, in musicological analysis or commercial application in song recognition. Within NIME, several projects exist seeking to make these techniques useful in real-time music making. However, we debate whether the functionally-oriented approaches inherited from engineering domains that much machine listening research manifests is fully suited to the exploratory, divergent, boundary-stretching, uncertainty-seeking, playful and irreverent orientations of many artists. To explore this, we engaged in a concerted collaborative design exercise in which many different listening algorithms were implemented and presented with input which challenged their customary range of application and the implicit norms of musicality which research can take for granted. An immersive 3D spatialised multichannel environment was created in which the algorithms could be explored in a hybrid installation/performance/lecture form of research presentation. The paper closes with reflections on the creative value of 'hijacking' formal approaches into deviant contexts, the typically undocumented practical know-how required to make algorithms work, the productivity of a playfully irreverent relationship between engineering and artistic approaches to NIME, and a sketch of a sonocybernetic aesthetics for our work

Analysis and resynthesis of polyphonic music

This thesis examines applications of Digital Signal Processing to the analysis, transformation, and resynthesis of musical audio. First I give an overview of the human perception of music. I then examine in detail the requirements for a system that can analyse, transcribe, process, and resynthesise monaural polyphonic music. I then describe and compare the possible hardware and software platforms. After this I describe a prototype hybrid system that attempts to carry out these tasks using a method based on additive synthesis. Next I present results from its application to a variety of musical examples, and critically assess its performance and limitations. I then address these issues in the design of a second system based on Gabor wavelets. I conclude by summarising the research and outlining suggestions for future developments

Radial Basis Function Networks for Conversion of Sound Spectra

In many advanced signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN) is proposed for the modeling of the spectral changes (or conversions) related to the control of important sound parameters, such as pitch or intensity. The identification of such conversion functions is based on a procedure which learns the shape of the conversion from few couples of target spectra from a data set. The generalization properties of RBFNs provides for interpolation with respect to the pitch range. In the construction of the training set, mel-cepstral encoding of the spectrum is used to catch the perceptually most relevant spectral changes. Moreover, a singular value decomposition (SVD) approach is used to reduce the dimension of conversion functions. The RBFN conversion functions introduced are characterized by a perceptually-based fast training procedure, desirable interpolation properties and computational efficiency

Singing voice resynthesis using concatenative-based techniques

Tese de Doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201

A computational framework for sound segregation in music signals

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200