Scott Seyfarth is a data scientist with 12 years of experience at the intersection of data science, statistics, linguistics and speech science, currently applying those skills at AWS in the New York City area. He holds a Ph.D. in Linguistics and has a strong academic track record including postdoctoral research and a visiting professorship, with published work on behavioral experiments and multilingual speech/text corpora. Scott bridges research and production: he’s contributed deep audio analysis improvements to the widely used librosa library, enhancing time-frequency tools and test coverage. His background in phonetics and musicology gives him uncommon domain insight for building robust speech and language models and turning experimental methods into scalable solutions.
12 years of coding experience
3 years of employment as a software developer
University of California San Diego
Bachelor’s Degree, Linguistics, Musicology, Bachelor’s Degree, Linguistics, Musicology at Northwestern University
Contributions:10 commits, 2 PRs, 19 comments in 5 months
Contributions summary:Scott contributed significantly to the `librosa` library, focusing on improving the time-frequency analysis functionalities. Their work involved modifying core spectrum analysis functions, specifically allowing them to accept pre-computed STFTs and introducing new options for the `reassigned_spectrogram` function, like clipping and handling of NaN values. They also improved the library's test suite by adding comprehensive tests for the reassignment functions, including tests for edge cases and ensuring cross-version compatibility.
Contributions:2 pushes, 1 branch, 2 comments in 6 years 11 months
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