Scholarship & Research
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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 A system to eavesdrop on marmosets(Montana State University - Bozeman, College of Engineering, 2015) Casebeer, Christopher Ness; Chairperson, Graduate Committee: Ross K. SniderThis masters thesis describes developing a custom digital recording system to record the vocalizations and behavior of marmosets, which are small primates native to the northeast of Brazil. Animal behavior scientists have traditionally studied communications between only a 'sender' and a 'receiver'. Animal communications however are hypothesized to occur in communication networks involving more than just a pair of animals. In this project a miniaturized recording system aimed at acquiring data to study the communication networks of the common marmoset is underway. The acoustic recording collar project aims to develop a wearable recording embedded platform for a freely behaving primate. A custom embedded platform utilizing a field programmable gate array (FPGA) has been developed to prototype the system. A hardware description language (HDL) has been used to create the FPGA architecture for the collar application, which in this case is the VHSIC Hardware Description Language (VHDL). Sensors used and developed for this application include a global positioning system (GPS), inertial measurement unit (IMU), and digital MEMS microphone. These sensors provide position and accurate time information, behaviorally related motion information, and the acoustic environment of the marmoset. This data comprises the Behavioral Acoustic Biome of the marmoset. Storage of the behavioral acoustic biome data occurs on a local microSD ash memory card. A printed circuit board of footprint 1.36 by 1.18 inches has been completed and the system will be soon fitted to a 3D printed collar wearable by the marmoset. Demonstration of sensor data logging to the microSD ash has been completed. Other developments of the embedded system are ongoing. Ultimately, fitting multiple wearable devices across a troop of freely behaving marmosets will allow novel studies of communication networks in the common marmoset to be undertaken.