Last Friday, when we were just winding up for the end of the week we started getting a large number of messages on our website chat app, and also a a huge spike in the number of hits on our website (1283.05%), and in particular from our audio frequency spectrum analyser called Spectrumview.
Had we been hacked? Had some rivettingly interestingly pictures of the intimate details of audio analysis been unwittingly released on our webpage? Thankfully not. Our app had been used by the extremely talented stand-up comedian and maths communicator Matt Parker () to measure how fast he could get a fidget spinner to go. This video had hundreds of thousands of views each day, and just under 300,000 at the time of writing.
This is a brilliant video that shows how to use Spectrumview to calculate the frequency and thereby the speed of the tips of the fidget spinner. We are delighted to see such a weird and wonderful use for our little app.
Before you ask, we don’t have an Android version. There are no plans to have one just yet, but we may be persuaded. If you ask nicely.
The Linguistic Data Consortium (LDC), USA and Oxford Wave Research (UK) are proud to announce a new collaboration. Oxford Wave Research (OWR) is an audio and speech R&D company based in Oxford, UK that works on audio processing and speaker diarization and recognition. This collaboration encompasses the use of LDC’s speech corpora and OWR’s audio fingerprinting, speaker diarization and recognition software.
In particular, LDC will use OWR’s audio fingerprinting technology as part of the MADCAT software (Multimedia Audio Duplication & Content Analysis Tool) to find repeated content in broadcast data including audio signatures that mark program boundaries.
The OWR Research Director, Dr Anil Alexander says,
“The OWR team is really looking forward to working with LDC on this exciting real-world application of audio fingerprinting of short utterances.”
LDC Executive Director Chris Cieri says
“The Consortium continually looks for new ways to integrate speech technology into data collection and annotation processes to improve speed, scale and quality while avoiding bias. We are excited by the increased capability that OWR tools offer.”