[Week Seven] How to Train Your Bat

Diana joins me this week to take on the task of training Bruce, our moody artificial echolocator. One of our biggest goals was to create a large dataset— 100,000 audio-depth samples from various rooms in the ISAT/CS building. Below are the problems we dealt with and the solutions we worked at: The current configuration of … Continue reading [Week Seven] How to Train Your Bat

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[Week Five] Divide It By Twenty…

This week's blog post will provide a brief overview of the project and news about breakthrough (!!!) that occurred this morning by Dr. Sprague. LET'S TALK ABOUT BATS. Bats have strong echolocating abilities that allow them to sense the physical world around them without having to use their eyes. Different bats emit different frequencies of calls; the … Continue reading [Week Five] Divide It By Twenty…

[Week Four] Trial and Error and Trial and Error

The big question I tried to answer this week: Can raw audio samples compete with spectrograms as inputs to convolutional neural networks (CNN)? Although I don't yet have the conclusive data needed to pick a team, I have some bias for the raw audio, which trains faster by nature of its structure. I'm also grateful … Continue reading [Week Four] Trial and Error and Trial and Error

[Week Three] How Many Weights Does It Take to Overwhelm a GPU?

For a lengthy period of time earlier this week, there was a series of high frequency chirps roaming around the CS hallway. That was the sound of science. It was also the sound of Dr. Sprague collecting audio samples for the echolocation project (thank you!). Once I had over 13,000 samples at my disposal (thank … Continue reading [Week Three] How Many Weights Does It Take to Overwhelm a GPU?