Woman Deals With An Epic Fight Between Her Two Cats During Live TV Interview

Talk about being professional! 

Jennylle Tupaz is the president and CEO of Ayala land malls in the Philippines but she's also the proud owner of two adorable kitties, Bella Luna and Nala. 

A few days ago, during a live TV interview from home, her two cats decided to trade blows right behind her. 

But the cool CEO managed to keep her composure and remained steadfast, with only glancing to her side periodically. 


The video she shared on her twitter page obviously went viral. 


Woman Deals With An Epic Fight Of Her Two Cats During Live TV Interview | CTV News Vancouver NEWS @CTVVancouver VANCOUVER President and CEO Jennylle Tupaz doing an interview her two cats decide hash out on live TV. PG SOURCE TWITTER/@nikkibigornia NASA LINYA JENNYLLE TUPAZ PRESIDENT CEO, AYALA LAND MALLS INC. PAGBILLNG ALAK SA OC LIMITADO MULA 1PM HANGGANG 5PM METGUOR CAT FIGHT ERUPTS ON LIVE TV INTERVIEW O MAY MGA KONDISYON >
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Dog owner makes aa
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Henry Conklin loves his dog and wants to share the canine's voice with the world. Modern social media and Conklin's technological know-how make that possible.

The mathematics/computer science undergraduate student at the University of Virginia regularly engages in competitive programming and decided to use these skills to make a Twitter account that his dog Oliver could 'control'.

@OliverBarkBark tweets every time Oliver barks. So far he's had some very insightful things to say:

Conklin describes the process by which he achieved this feat on his website:

By connecting a Rasberry Pi, a wifi dongle, and a microphone, I was able to make a system that automatically detected, filtered, and published each and every one of Oliver's deafening vocalizations.

The full process from bark to tweet takes three steps. First is recording. I have the raspberry pi listening continuously and triggering a recording once it hears a sound over a preset volume. Oliver barking is by far the loudest thing within several miles, so the volume threshold should be sufficient. However, the recordings are still triggered occasionally by unwanted junk. To guard against this, I needed to perform a second step to filter the barks from the junk.

I took a machine learning approach to filter out the barks. I built a model using the pyAudioAnalysis library and around a day's worth of barks (about 20). I then set up a bash script to run every ten minutes, classify each recorded sound, and forward the barks on to the next step.

Finally, the barks are forwarded to the twitter api (using python-twitter) and posted under the handle @OliverBarkBark (be sure to follow!). Currently the tweets are random strings composed of "bark," "ruff," and "woof." I plan to replace that with a bark-to-text translator that will likely produce similar results but be more accurate to Oliver's actual voice.

Being the cool guy that he seems, Conklin also made all the code it took to accomplish this publicly available.

He said the next step is puppy podcasting.

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