Neural Audio: Music Information Retrieval Using Deep Neural Networks

The use of deep neural networks has exploded in popularity recently. Thinking that music information retrieval should not be left out of this trend in machine learning, we explore two different applications of this technology in the field. The first we looked at was genre identificaton, using the initial categories of \u27popular music,\u27 \u27art music,\u27 and \u27traditional music.\u27 This was found to be a difficult problem - classifying music into these categories can be challenging even for experts, and assembling a large dataset for use in training represents a significant problem. The second approach we took to using these techniques was looking at instrument identification, specifically for the purpose of identifying the time and category (from guitar, vocal, or drum ) of solos in popular music

The physical basis for Parrondo's games

Several authors have implied that the original inspiration for Parrondo's games was a physical system called a ``flashing Brownian ratchet''. The relationship seems to be intuitively clear but, surprisingly, has not yet been established with rigor. In this paper, we apply standard finite-difference methods of numerical analysis to the Fokker-Planck equation. We derive a set of finite difference equations and show that they have the same form as Parrondo's games. Parrondo's games, are in effect, a particular way of sampling a Fokker-Planck equation. Physical Brownian ratchets have been constructed and have worked. It is hoped that the finite element method presented here will be useful in the simulation and design of flashing Brownian ratchets.Comment: 10 pages and 2 figure

Optimizing genetic algorithm strategies for evolving networks

This paper explores the use of genetic algorithms for the design of networks, where the demands on the network fluctuate in time. For varying network constraints, we find the best network using the standard genetic algorithm operators such as inversion, mutation and crossover. We also examine how the choice of genetic algorithm operators affects the quality of the best network found. Such networks typically contain redundancy in servers, where several servers perform the same task and pleiotropy, where servers perform multiple tasks. We explore this trade-off between pleiotropy versus redundancy on the cost versus reliability as a measure of the quality of the network.Comment: 9 pages, 5 figure

Exploring tradeoffs in pleiotropy and redundancy using evolutionary computing

Evolutionary computation algorithms are increasingly being used to solve optimization problems as they have many advantages over traditional optimization algorithms. In this paper we use evolutionary computation to study the trade-off between pleiotropy and redundancy in a client-server based network. Pleiotropy is a term used to describe components that perform multiple tasks, while redundancy refers to multiple components performing one same task. Pleiotropy reduces cost but lacks robustness, while redundancy increases network reliability but is more costly, as together, pleiotropy and redundancy build flexibility and robustness into systems. Therefore it is desirable to have a network that contains a balance between pleiotropy and redundancy. We explore how factors such as link failure probability, repair rates, and the size of the network influence the design choices that we explore using genetic algorithms.Comment: 10 pages, 6 figure