Acoustic Software Developer Intern
Blackberry QNX
May 2019 to Dec. 2019
I created a test framework in Python for proprietary voice enhancing and noise-control technology that is currently being used in release testing. I used Python to process audio files and simulate acoustic conditions and quantified the performance by aggregating the results of the processed audio being run through several voice recognition services such as AWS Transcribe, Google Speech-to-Text, CMU Sphinx, and Sensory TrulyHandsFree. Additionally, I was able to add functionality to the C++ utility scripts in the voice-enhancement library.