Spatial and Content-based Audio Processing using Stochastic Optimization Methods

Stochastic optimization (SO) represents a category of numerical optimization approaches, in which the search for the optimal solution involves randomness in a constructive manner. As shown also in this thesis, the stochastic optimization techniques and models have become an important and notable paradigm in a wide range of application areas, including transportation models, financial instruments, and network design. Stochastic optimization is especially developed for solving the problems that are either too difficult or impossible to solve analytically by deterministic optimization approaches. In this thesis, the focus is put on applying several stochastic optimization algorithms to two audio-specific application areas, namely sniper positioning and content-based audio classification and retrieval. In short, the first application belongs to an area of spatial audio, whereas the latter is a topic of machine learning and, more specifically, multimedia information retrieval. The SO algorithms considered in the thesis are particle filtering (PF), particle swarm optimization (PSO), and simulated annealing (SA), which are extended, combined and applied to the specified problems in a novel manner. Based on their iterative and evolving nature, especially the PSO algorithms are often included to the category of evolutionary algorithms. Considering the sniper positioning application, in this thesis the PF and SA algorithms are employed to optimize the parameters of a mathematical shock wave model based on observed firing event wavefronts. Such an inverse problem is suitable for Bayesian approach, which is the main motivation for including the PF approach among the considered optimization methods. It is shown – also with SA – that by applying the stated shock wave model, the proposed stochastic parameter estimation approach provides statistically reliable and qualified results. The content-based audio classification part of the thesis is based on a dedicated framework consisting of several individual binary classifiers. In this work, artificial neural networks (ANNs) are used within the framework, for which the parameters and network structures are optimized based the desired item outputs, i.e. the ground truth class labels. The optimization process is carried out using a multi-dimensional extension of the regular PSO algorithm (MD PSO). The audio retrieval experiments are performed in the context of feature generation (synthesis), which is an approach for generating new audio features/attributes based on some conventional features originally extracted from a particular audio database. Here the MD PSO algorithm is applied to optimize the parameters of the feature generation process, wherein the dimensionality of the generated feature vector is also optimized. Both from practical perspective and the viewpoint of complexity theory, stochastic optimization techniques are often computationally demanding. Because of this, the practical implementations discussed in this thesis are designed as directly applicable to parallel computing. This is an important and topical issue considering the continuous increase of computing grids and cloud services. Indeed, many of the results achieved in this thesis are computed using a grid of several computers. Furthermore, since also personal computers and mobile handsets include an increasing number of processor cores, such parallel implementations are not limited to grid servers only

Multimodal music information processing and retrieval: survey and future challenges

Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modalities include audio recordings, symbolic music scores, mid-level representations, motion, and gestural data, video recordings, editorial or cultural tags, lyrics and album cover arts. This paper critically reviews the various approaches adopted in Music Information Processing and Retrieval and highlights how multimodal algorithms can help Music Computing applications. First, we categorize the related literature based on the application they address. Subsequently, we analyze existing information fusion approaches, and we conclude with the set of challenges that Music Information Retrieval and Sound and Music Computing research communities should focus in the next years

Neural Translation of Musical Style

Music is an expressive form of communication often used to convey emotion in scenarios where "words are not enough". Part of this information lies in the musical composition where well-defined language exists. However, a significant amount of information is added during a performance as the musician interprets the composition. The performer injects expressiveness into the written score through variations of different musical properties such as dynamics and tempo. In this paper, we describe a model that can learn to perform sheet music. Our research concludes that the generated performances are indistinguishable from a human performance, thereby passing a test in the spirit of a "musical Turing test"

Singing voice resynthesis using concatenative-based techniques

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

Computational composition strategies in audiovisual laptop performance

We live in a cultural environment in which computer based musical performances have become ubiquitous. Particularly the use of laptops as instruments is a thriving practice in many genres and subcultures. The opportunity to command the most intricate level of control on the smallest of time scales in music composition and computer graphics introduces a number of complexities and dilemmas for the performer working with algorithms. Writing computer code to create audiovisuals offers abundant opportunities for discovering new ways of expression in live performance while simultaneously introducing challenges and presenting the user with difficult choices. There are a host of computational strategies that can be employed in live situations to assist the performer, including artificially intelligent performance agents who operate according to predefined algorithmic rules. This thesis describes four software systems for real time multimodal improvisation and composition in which a number of computational strategies for audiovisual laptop performances is explored and which were used in creation of a portfolio of accompanying audiovisual compositions