Theses and Dissertations at Montana State University (MSU)
Permanent URI for this communityhttps://scholarworks.montana.edu/handle/1/732
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Item Enabling rapid prototyping of audio signal processing systems using system-on-chip field programmable gate arrays(Montana State University - Bozeman, College of Engineering, 2020) Vannoy, Trevor Charles; Chairperson, Graduate Committee: Ross K. SniderSystem-on-Chip Field Programmable Gate Arrays are excellent devices for high performance, low latency signal processing. Unfortunately, they are notoriously difficult to use, requiring significant hardware and software engineering expertise. To address these challenges, a development framework is created that utilizes graphical programming and automatic code generation; this framework reduces development time and reduces the need to be an expert in Field Programmable Gate Arrays. A sound effects processor and a real-time audio beamformer were created to showcase the development framework and serve as reference designs for other developers. The development framework, coupled with open source audio hardware, enables both experts and non-experts to rapidly prototype audio signal processing systems using System-on-Chip Field Programmable Gate Arrays.Item A customizable artificial auditory fovea(Montana State University - Bozeman, College of Engineering, 2018) Casebeer, Christopher Ness; Chairperson, Graduate Committee: Ross K. SniderWe, as humans, can separate and attend to audio sources in mixtures of sounds and noise. We can listen distinctly to a friend at a party in a sea of background noise and conversations. Human auditory neurology exceeds even state-of-the-art audio algorithms. How are we able to do this? This dissertation takes inspiration from biology to frame a novel audio processing front-end. Neurobiology shows that auditory neurons isolate signal onsets, timing, frequency, amplitude and modulation characteristics. Why is it then that many standard processing methods choose to ignore this information or make the assumption that machine learning will extract it regardless of input processing? This dissertation uses time-frequency analysis principles towards building a new front-end aimed at preserving these fine temporal and spectral details of the original signal to improve audio system detection and recognition. The system allows keeping the fine frequency and time characteristics of a signal during analysis, while allowing customization of how much and where this resolution is kept. Like biology, this front-end can dedicate resources to detecting important signal events. It can over represent or foveate regions of the time-frequency plane that are important to the signal processing task at hand. These fine details are hypothesized to help enable audio learning algorithms to detect the fine nuances that distinguish musical instruments, determine the characteristics of a specific persons voice, or even detect the emotional state of a person. This customizable auditory fovea aims to mimic the powerful detection capability found in biology which is in contrast to standard methods in audio signal processing.Item Computer based characterization of a spatial-spectral (s2) material signal processor(Montana State University - Bozeman, College of Engineering, 2006) Khallaayoun, Ahmed; Chairperson, Graduate Committee: Richard WolffThe Spectrum Lab has developed a computer based model for a new generation processor where one of its applications promises improvement in current and future generation Radars. This processor is named the S2CHIP (Spatial Spectral Coherent Holographic Integrating Processor). The purpose of this work is to characterize the S2CHIP under different conditions in terms of signal strength, noise level and dynamic range. The characterization has been done using a new simulator developed at the Spectrum Lab based on the Maxwell-Bloch equations. This tool enabled us to simulate various effects based not only on the material properties but also effects based on the laser source and other components that make up the overall system. Laser beam geometry, material thickness, integration processing, and material and laser coherence time are addressed in this thesis. These simulations give a good measure on the performance of the S2CHIP.