A Parametric Sound Object Model for Sound Texture Synthesis

This thesis deals with the analysis and synthesis of sound textures based on parametric sound objects. An overview is provided about the acoustic and perceptual principles of textural acoustic scenes, and technical challenges for analysis and synthesis are considered. Four essential processing steps for sound texture analysis are identifi ed, and existing sound texture systems are reviewed, using the four-step model as a guideline. A theoretical framework for analysis and synthesis is proposed. A parametric sound object synthesis (PSOS) model is introduced, which is able to describe individual recorded sounds through a fi xed set of parameters. The model, which applies to harmonic and noisy sounds, is an extension of spectral modeling and uses spline curves to approximate spectral envelopes, as well as the evolution of parameters over time. In contrast to standard spectral modeling techniques, this representation uses the concept of objects instead of concatenated frames, and it provides a direct mapping between sounds of diff erent length. Methods for automatic and manual conversion are shown. An evaluation is presented in which the ability of the model to encode a wide range of di fferent sounds has been examined. Although there are aspects of sounds that the model cannot accurately capture, such as polyphony and certain types of fast modulation, the results indicate that high quality synthesis can be achieved for many different acoustic phenomena, including instruments and animal vocalizations. In contrast to many other forms of sound encoding, the parametric model facilitates various techniques of machine learning and intelligent processing, including sound clustering and principal component analysis. Strengths and weaknesses of the proposed method are reviewed, and possibilities for future development are discussed

Automatic sound synthesis using the fly algorithm

Our study is demonstrated a new type of evolutionary sound synthesis method. This work based on the fly algorithm, a cooperative co-evolution algorithm; it is derived from the Parisian evolution approach. The algorithm has relatively amended the position of individuals (the Flies) represented by 3-D points. The fly algorithm has successfully investigated in different applications, starting with a real-time stereo vision for robotics. Also, the algorithm shows promising results in tomography to reconstruct 3-D images. The final application of the fly algorithm was generating artistic images, such as digital mosaics. In all these applications, the flies’representation started for simple, 3-D points, to complex one, the structure of 9-elements. Our method follows evolutionary digital art with the fly algorithm in representing the pattern of the flies. They represented in a way of having their structure. This structure includes position, colour, rotation angle, and size. Our algorithm has the benefit of graphics processing units (GPUs) to generate the sound waveform using the modern OpenGL shading language

Model-based analysis of noisy musical recordings with application to audio restoration

This thesis proposes digital signal processing algorithms for noise reduction and enhancement of audio signals. Approximately half of the work concerns signal modeling techniques for suppression of localized disturbances in audio signals, such as impulsive noise and low-frequency pulses. In this regard, novel algorithms and modifications to previous propositions are introduced with the aim of achieving a better balance between computational complexity and qualitative performance, in comparison with other schemes presented in the literature. The main contributions related to this set of articles are: an efficient algorithm for suppression of low-frequency pulses in audio signals; a scheme for impulsive noise detection that uses frequency-warped linear prediction; and two methods for reconstruction of audio signals within long gaps of missing samples. The remaining part of the work discusses applications of sound source modeling (SSM) techniques to audio restoration. It comprises application examples, such as a method for bandwidth extension of guitar tones, and discusses the challenge of model calibration based on noisy recorded sources. Regarding this matter, a frequency-selective spectral analysis technique called frequency-zooming ARMA (FZ-ARMA) modeling is proposed as an effective way to estimate the frequency and decay time of resonance modes associated with the partials of a given tone, despite the presence of corrupting noise in the observable signal.reviewe

Synthesizer Parameter Approximation by Deep Learning

Synthesizers have been an essential tool for composers of any style of music including computer generated sound. They allow for an expansion in timbral variety to the orchestration of a piece of music or sound scape. Sound designers are trained to be able to recreate a timbre in their head using a synthesizer. This works well for simple sounds but becomes more difficult as the number of parameters required to produce a specific timbre increase. The goal of this research project is to formulate a method for synthesizers to approximate a timbre given an input audio sample using deep learning. The synthesizer should be able to modify its settings (oscillators, filters, LFOs, effects, etc.) to produce an audio signal as close to the input sample as possible. A cost function will measure the difference between the outputted audio signal from the learned synthesizer parameters and the original audio signal that is being mimicked

Play it Again: Evolved Audio Effects and Synthesizer Programming

Automatic programming of sound synthesizers and audio devices to match a given, desired sound is examined and a Genetic Algorithm (GA) that functions independent of specific synthesis techniques is proposed. Most work in this area has focused on one synthesis model or synthesizer, designing the GA and tuning the operator parameters to obtain optimal results. The scope of such inquiries has been limited by available computing power, however current software (Ableton Live, herein) and commercially available hardware is shown to quickly find accurate solutions, promising a practical application for music creators. Both software synthesizers and audio effects processors are examined, showing a wide range of performance times (from seconds to hours) and solution accuracy, based on particularities of the target devices. Random oscillators, phase synchronizing, and filters over empty frequency ranges are identified as primary challenges for GA based optimization

Techniques for automated parameter estimation in computational models of probabilistic systems

The main contribution of this dissertation is the design of two new algorithms for automatically synthesizing values of numerical parameters of computational models of complex stochastic systems such that the resultant model meets user-specified behavioral specifications. These algorithms are designed to operate on probabilistic systems – systems that, in general, behave differently under identical conditions. The algorithms work using an approach that combines formal verification and mathematical optimization to explore a model\u27s parameter space. The problem of determining whether a model instantiated with a given set of parameter values satisfies the desired specification is first defined using formal verification terminology, and then reformulated in terms of statistical hypothesis testing. Parameter space exploration involves determining the outcome of the hypothesis testing query for each parameter point and is guided using simulated annealing. The first algorithm uses the sequential probability ratio test (SPRT) to solve the hypothesis testing problems, whereas the second algorithm uses an approach based on Bayesian statistical model checking (BSMC). The SPRT-based parameter synthesis algorithm was used to validate that a given model of glucose-insulin metabolism has the capability of representing diabetic behavior by synthesizing values of three parameters that ensure that the glucose-insulin subsystem spends at least 20 minutes in a diabetic scenario. The BSMC-based algorithm was used to discover the values of parameters in a physiological model of the acute inflammatory response that guarantee a set of desired clinical outcomes. These two applications demonstrate how our algorithms use formal verification, statistical hypothesis testing and mathematical optimization to automatically synthesize parameters of complex probabilistic models in order to meet user-specified behavioral propertie

Perception of attributes in real and synthetic string instrument sounds

This thesis explores the perceptual features of natural and synthetic string instrument sounds. The contributions are in formal listening experiments on a variety of features in musical sounds that have not been studied in detail previously. The effects of inharmonicity on timbre and pitch have been measured. The results indicate that the implementation of inharmonicity is not always necessary. The timbre effect is more salient in natural instruments, but for high tones a pitch difference may also be detected. Guidelines were given for compensation of the pitch effect. A perceptual study of the decaying parameters showed that large deviations from the reference value are tolerated perceptually. The studies on the audibility of initial pitch glides and dual-polarization effects provides practical knowledge that helps in the implementation of these features in digital sound synthesis. Related to expression rather than basic string behavior, the study on perception-based control of the vibrato parameters has a sligthly different background. However, all of the studied features are more or less player-controlled by different ways of plucking the string or pressing the key. The main objective of the thesis is to find answers to current problems in digital sound synthesis, such as parameter quantization. Another aim is to gain more general understanding of how we perceive musical sounds.reviewe