Unsupervised learning to quantify differences in song learning of experimental zebra finch populations
Author(s)Ennis, Michaela (Michaela M.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Michale S. Fee.
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Zebra finch song learning is a common model of motor learning processes, but quantification of song properties is lacking, particularly for comparison of experimental populations across development. Sparse convolutional feature extraction, a method previously used to analyze other natural sounds, is applied to zebra finch song here. The results of feature extraction were used to develop metrics that were applied to zebra finch song from across both normal and isolate development. As expected, adult control song was substantially different from adult isolate song in all metrics. More interestingly, differences in some metrics were seen between the two as early in development as recordings were taken, suggesting that differences exist prior to obvious abnormalities appearing in the song spectrogram. Overall, these results provide interesting ideas about isolate song learning, and act as a proof of concept for the use of sparse convolutional learning to compare bird populations.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 95-98).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.