Orientador: Jonatas Manzolli / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Artes / Made available in DSpace on 2018-08-11T01:31:44Z (GMT). No. of bitstreams: 1 Porres_AlexandreTorres_M.pdf: 11455716 bytes, checksum: ea6618983f2e1d8ab253db22a60f9f9f (MD5) Previous issue date: 2007 / Resumo: Foram estudados, na presente pesquisa, Sistemas de Afinação com o intuito de investigar a aplicação desse conhecimento em Processos Criativos. Para tal, desenvolvemos uma distinção de abordagens composicionais na música do Século XX, e também estudamos o mecanismo de construção de escalas adotando, principalmente, o estudo da Psicoacústica (i.e. Modelo de Dissonância Sensorial), que propiciou o desenvolvimento de uma ferramenta computacional tanto de análise de sons e Sistemas de Afinação, quanto de criação sonora. Os estudos serviram de base para o desenvolvimento de Processos Criativos com comentários de acordo com os objetivos de pesquisa / Abstract: On the present research, Tuning Systems were studied in order to investigate its application in Creative Processes. For such, we created a distinction of compositional approaches in the music of the 20th Century, and we also studied mechanisms of scale development by mainly adopting Psychoacoustics (i.e. Roughness Model), which enabled the development of a computational tool for the analysis of sounds and Tuning Systems, as well as musical creation. The studies have been applied in Creative Processes with comments and discussions over the research¿s objectives / Mestrado / Mestre em Música

Digital watermarking is a technique for digital multimedia copyright protection. The robustness and the imperceptibility are the main requirements of the watermark. This thesis deals with watermarking audio signals using artificial neural networks. There is described audio watermarking method in the DCT domain. Method is based on human psychoacoustic model and the techniques of neural networks.

Digital watermarking is a technique for digital multimedia copyright protection. The robustness and the imperceptibility are the main requirements of the watermark. This thesis deals with watermarking audio signals using artificial neural networks. There is described audio watermarking method in the DCT domain. Method is based on human psychoacoustic model and the techniques of neural networks.

his thesis describes Perceptual Audio Coding of MPEG1 Layer 3 format (ISO/IEC 11172-3), principles and algorithms of psychoacoustic model. MATLAB application for modeling of Psychoacoustic model 2 of this audio format is developed.

This thesis deals with the issue of the creation of a programme that would simulate the distortion that appears during the process of lossy audio coding. As the environment for the creation, the MATLAB programming language has been chosen. An encoder, which changes the subjective signal quality according to customer preferences for the bitrate, has been created as a practical part of this thesis. Its function is based on a dynamic bit allocation technique and includes an optional window switching algorithm. The theoretical background for the creation of the programme consists of an explanation of the main principles of lossy coding with emphasis on MPEG1 layer 3 operating principles. The practical chapter describes how the created programme and its parts work, and it includes results of the run quality testing. The testing was conducted using the objective assessment method PEMO-Q, and consisted of comparing the objective quality of the programme’s outputs to the quality of samples on which a regular MP3 encoder with identical settings was used.

In this work is presented technique for embedding digital watermark in digital audio signals. Digital watermark must be imperceptible and should be robust against attacks and other types of distortion. Algorithm is implemented for embedding digital watermark using technique spread-spectrum and psychoacoustic model ISO-MPEG I layer I. Robustness was tested for filtering signal, MP3 compression and resample method.

This thesis deals with description of human hearing, audio and speech codecs, description of objective measure of quality and practical comparison of codecs. Chapter about audio codecs consists of description of lossless codec FLAC and lossy codecs MP3 and Ogg Vorbis. In chapter about speech codecs is description of linear predictive coding and G.729 and OPUS codecs. Evaluation of quality consists of description of segmental signal-to- noise ratio and perceptual evaluation of quality – WSS and PESQ. Last chapter deals with description od practical part of this thesis, that is comparison of memory and time consumption of audio codecs and perceptual evaluation of speech codecs quality.

The master's thesis deals with the restoration of audio signals that have been damaged by clipping. Used methods are based on sparse representations of signals. The introduction of the thesis explains the issue of clipping and mentions the list of already existing methods that solve declipping, which are followed by the thesis. In the next chapter, the necessary theory of sparse representations and the proximal algorithms is described, including specific representatives from the category of convex optimization problems. The thesis contains declipping algorithm implemented in Matlab software environment. Chosen method for solving the task uses the Condat algorithm or Generic proximal algorithm for convex optimization and solves minimization of sum of three convex functions. The result of the thesis is five versions of algorithm and three of them have implemented psychoacoustic model for results improvement. For each version has been found optimal setting of parameters. The restoration quality results are evaluated using objective measurements like SDR and PEMO-Q and also using subjective listening test.

The dissertation thesis focuses on objective assessment and reduction of disturbing background noise in a musical signal. In this work, a new algorithm for the assessment of background noise audibility is proposed. The listening tests performed show that this new algorithm better predicts the background noise audibility than the existing algorithms do. An advantage of this new algorithm is the fact that it can be used even in the case of a general audio signal and not only musical signal, i.e. in the case when the audibility of one sound on the background of another sound is assessed. The existing algorithms often fail in this case. The next part of the dissertation thesis deals with an adaptive segmentation scheme for the segmentation of long-term musical signals into short segments of different lengths. A new adaptive segmentation scheme is then introduced here. It has been shown that this new adaptive segmentation scheme significantly improves the subjectively perceived quality of the musical signal from the output of noise reduction systems which use this new adaptive segmentation scheme. The quality improvement is better than that achieved by other segmentation schemes tested.