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

Do Organisational Climate and Strategic Orientation Moderate the Relationship Between Human Resource Management Practices and Productivity?

Contingency formulations of Human Resource Management (HRM) theory suggest thatthe effectiveness of HRM practices should vary across firms. This study examinedwhether the relationship between HRM practices and productivity in manufacturingcompanies is contingent upon organizational climate and strategic orientation.Information on HRM, organizational structure, and competitive strategy was collected byinterviewing senior managers, whilst organizational climate was assessed via employeesurveys. Although organizational climate and HRM practices were both positivelyassociated with subsequent productivity, the relationship between HRM practices andsubsequent productivity was stronger for firms with a poor climate.Human Resource Management, organisational structure, organisationalclimate, productivity

Algorithms for Near-Term and Noisy Quantum Devices

Quantum computing promises to revolutionise many fields, including chemical simulations and machine learning. At the present moment those promises have not been realised, due to the large resource requirements of fault tolerant quantum computers, not excepting the scientific and engineering challenges to building a fault tolerant quantum computer. Instead, we currently have access to quantum devices that are both limited in qubit number, and have noisy qubits. This thesis deals with the challenges that these devices present, by investigating applications in quantum simulation for molecules and solid state systems, quantum machine learning, and by presenting a detailed simulation of a real ion trap device. We firstly build on a previous algorithm for state discrimination using a quantum machine learning model, and we show how to adapt the algorithm to work on a noisy device. This algorithm outperforms the analytical best POVM if ran on a noisy device. We then discuss how to build a quantum perceptron - the building block of a quantum neural network. We also present an algorithm for simulating the Dynamical Mean Field Theory (DMFT) using a quantum device, for two sites. We also discuss some of the difficul- ties found in scaling up that system, and present an algorithm for building the DMFT ansatz using the quantum device. We also discuss modifications to the algorithm that make it more ‘device-aware’. Finally we present a pule-level simulation of the noise in an ion trap device, designed to match the specifications of a device at the National Physical Laboratory (NPL), which we can use to direct future experimental focus. Each of these sections is preceded by a review of the relevant literature

Mining: A Naval Strategy

The following historic narrative and analysis offer the reader not only a valuable review of past successes and failures of mine campaigns, but also a series of unique insights into possible roles for the mine in contemporary warfare

Project Labyrinth: Exploration of Modulating Guitar Signals

The overall objective of this project is to create a guitar pre-amplifier in the casing of an effects pedal. The pedal must be able to modulate amplitude and pitch. More specifically, the design for this pedal will include floating frequency filtering, phase shifting, and amplitude modulation