The Bloop was the sound of an icequake-an iceberg cracking and breaking away from an Antarctic glacier! With global warming, more and more icequakes occur annually, breaking off glaciers, cracking and eventually melting into the ocean. It was there, on Earth’s lonely southernmost land mass, that they finally discovered the source of those thunderous rumbles from the deep in 2005. Was the Bloop from secret underwater military exercises, ship engines, fishing boat winches, giant squids, whales, or a some sea creature unknown to science?Īs the years passed, PMEL researchers continued to deploy hydrophones ever closer to Antarctica in an ongoing effort to study the sounds of sea floor volcanoes and earthquakes. Scientists from NOAA’s Pacific Marine Environmental Laboratory (PMEL) were eager to discover the sound's origin, but with about 95 percent of the ocean unexplored, theories abounded. Not only was it loud, the sound had a unique characteristic that came to be known as “the Bloop.” Using hydrophones, or underwater microphones, that were placed more than 3,219 kilometers apart across the Pacific, they recorded numerous instances of the noise, which was unlike anything they had heard before. Or, disable the moving average filter by going into ei_run_classifier.In 1997, researchers listening for underwater volcanic activity in the southern Pacific recorded a strange, powerful, and extremely loud sound. However, if your dataset is unbalanced (there’s a lot more noise / unknown than in your dataset) the ML model typically manages to only find your keyword in the 'center' window, and thus we filter it out as a false positive. if we do 3 classifications per second you’ll see your keyword potentially classified three times (once at the start of the audio file, once in the middle, once at the end). When running in continuous mode we run a moving average over the predictions to prevent false positives. Features: Tests and trains your reflexes. I couldn’t have been more than 6 years old at this point. I’ve had an interest in gaming for as long as I can remember, in fact one of my earliest memories was playing Roland In The Caves, Oh Mummy and Barbarian on the Amstrad CPC. #Bleep bloop free#Use bombs and time tiles to slow down time and keep pushing your high score One of the best free casual games to kill time or have fun on your daily commute. bleep bloop A site dedicated to my mostly retro gaming passion. As the dots pop up faster it gets harder to keep up. TIDAL is the first global music streaming service with high fidelity sound, hi-def video quality, along with expertly curated playlists and original content. #Bleep bloop code#Is your model not working properly? Then this is probably due to dataset imbalance (a lot more unknown / noise data compared to your keyword) in combination with our moving average code to reduce false positives. This casual bubble popping game is a combination of action and puzzle. A light-hearted and playful puzzle game about working. Poor performance due to unbalanced dataset? Meet Bleep and Bloop as they help each other overcome all the challenges that stand in their way. Your micro:bit now responds to your own keyword □. Note: the name used for the label of the training-set should correspond exactly to the #define INFERENCING_KEYWORD If you've picked a different keyword, change this in source/MicrophoneInferenceTest.cpp. #Bleep bloop zip file#Remove source/edge-impulse-sdk, source/model-parameters and source/tflite-model.ĭrag the content of the ZIP file into the source folder. Once you've trained a model go to Deployment, and select C++ Library. Go back to Data acquisition and now record your new keyword many times using your phone at frequency 11000Hz.Īfter uploading click the three dots, select Split sample and click Split to slice your data in 1 second chunks. Go to Data acquisition, and click the 'Upload' icon.Ĭhoose all the WAV items in the dataset and leave all other settings as-is. Sign up for an account and open your project.ĭownload the base dataset - this contains both 'noise' and 'unknown' data that you can use. You can build new models using Edge Impulse. The ML model that powers this project is available on Edge Impulse: Micro:bit LIVE 2020. $ docker run -rm -v "%cd%":/data microbit_ei_buildĪnd flash the binary to your micro:bit, by dragging MICROBIT.hex onto the MICROBIT disk drive.
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