BUT IN YOUR CASE IT COULD BE INTERESTING YEAH UNLESS PEOPLE PUT SOMETHING ON THE MICROPHONE BUT UM THEY WON'T HAVE NICE LIGHT UH LIGHTING ON IN FRONT OF THEM THEY WOULD MAYBE CAREFUL RIGHT BUT THAT I'M I THINK AFTER THE MEETING WE CAN GO AND TALK TO OLIVIER ABOUT THAT I'M NOT THE PERSON FOR HARDWARE NO SO MAYBE W UH YOU COULD PRESENT A DIFFERENT WORK UH YOU HAVE DONE AND UH I DON'T KNOW HOW THEY ARE YEAH I'LL T I'LL TRY TO PRESENT UH I GUESS IT CORRESPONDS TO YOUR QUESTIONS SO YOU TOLD US IT WAS DIFFERENT PIECES MORE THAN ONE INTEGRATED DEVICE UM YOU ASKED ABOUT HOW TO DETECT UM ACTIVITY CURRENTLY YOU'RE THRESHOLDING THE T MAXIMUM OF YOUR WAVE FORM RIGHT MM NOT MAXIMUM THE THE AVERAGE ON THE ON THE PACKETS OKAY AND YOU'RE LOOKING FOR A PEAK UH YES THE THE I TAKE THE THE B I G WHEN I GO UP TO THE THE CURVE UH I'M UH ON THE AT THE LOCAL MAXIMUM I TAKE IT THEN THE NEXT UH LOCAL MINIMUM AND UH TAKING THIS UH THIS RANGE DOING AVERAGE ON IT UH DIFFERENCE WHAT IS THE DIFFERENCE IT'S A KIND OF FILTERING ALREADY YEAH YEAH AND YOUR SECOND QUESTION UH WAS ABOUT F. F. T. WHAT CAN BE DONE WITH F. F. T. SO UM THE APPROACH WE'RE TAKING HERE IS TO ANSWER BOTH QUESTIONS THE SAME TIME WE USE F. F. T. TO DO DETECTION WE DON'T USE THE WAVE FORM THE RAW WAVE FORM SO UM UH WE SPLIT UM WE SPLIT THE SIGNAL IN SMALL FRAMES LIKE SIXTEEN MILLISECOND SIXTEEN OKAY UM EACH FRAME IS TAKING THIRTY TWO MILLISECOND OF SIGNAL JUST TO GIVE AN IDEA AND THERE IS A ONE FRAME EVERY SIXTEEN MILLISECOND AND THEY OVERLAP OF FIFTY PERCENT OKAY UM SO IT'S THIRTY TWO MILLISECOND FRAMES TAKEN EACH EACH SIXTEEN MILLISECOND WELL TH THESE ARE DETAILS IT'S JUST TO GIVE AN IDEA UM SO WHY ARE YOU OVERLAPPING WELL IT'S UH THE THE C BUT UH WHAT IS THE MAIN INTEREST MM-HMM SO THE BEGINNING AND THE END IT IT I IT'S NOT CRAPPY IT'S JUST NOT VERY MUCH REP REPRESENTED BECAUSE YOU APPLY UM A WINDOW WHICH HAS THIS SHAPE HAVING WINDOW SO THEY'RE VERY YEAH NO I'M SAYING UH WHEN YOU TAKE YOUR SIGNAL YOU TAKE ONE FRAME YOU APPLY THE WINDOW UH AND THE WINDOW IS SIMPLY UH COEFFICIENTS YOU GIVE AND YOU GIVE HIGHER COEFFICIENT TO THE MIDDLE THAN TO THE EXTREME SO IF YOU DON'T DO OVERLAP Y YOU WILL LOSE INFORMATION AT THE BEGINNING AND THE END OKAY OKAY OKAY BUT YOU DON'T HAVE TO DO OVERLAP I MEAN UH IT'S UM YES AND THEN I JUST UH BECAUSE UH WE ARE USING UM UH AN OPEN S AN OPEN SOURCE FRAMEWORK TO DO THIS THERE IS UH SOMETHING I DON'T KNOW IF YOU KNOW I KNOW IT UH CALLS SPHINX THAT IS DOING UH S SORRY IS IT FROM C. M. U. UM UH YEAH DOING UM VOICE RECOGNITION AND UH I JUST USE IT FOR THE FOR THE FOR THE MICROPHONE AND THE ACQUISITION SO UH THERE IS UH ALREADY UH AN F. F. T. UH MODULE IMPLEMENTED IN IT I DIDN'T TEST IT BUT UM UH UM UH WHAT WE DO RELEASE A PHASE DOMAIN ANALYSIS WE ONLY USE THE PHASE BETWEEN THE MICROPHONES UM Y YES FOR THE FOR THE THE POSITION YEAH BUT ALSO FOR DETECTION ALSO FOR DETECTION UM WE HAVE A WAY SO UM I DON'T THINK I SHOULD GET INTO DETAILS BUT BASICALLY THE BEGINNING IS YOUR SIGNAL WHICH YOU SLICE INTO FRAME YOU DO F. F. T. ON EACH MIC AND THE END IS UM A NUMBER OF UH FREQUENCY BEAMS WHICH ARE USED BY EACH PERSON TO UH EXPLAIN ROUGHLY S SO WHEN YOU SPEAK SPEECH IS WIDE BAND SO UM THE MORE ACTIVE YOU ARE THE MORE BAND WIDTH YOU USE SO YOU WILL GET A LARGE VALUE OF BAND WIDTH FOR PEOPLE WHO SPEAK AND UH FOR THE OTHERS IT WILL RANDOM IT WILL BE A SMALL VALUE CORRESPONDING TO BREATH OR A LITTLE NOISE OR JUST BACKGROUND NOISE AND SO UH OKAY BUT YOU'RE TALKING ABOUT THE MICROPHONE ARRAY HERE 'CAUSE ONE PROBLEM IF YOU USE ENERGY BASED METHODS WHICH IS PROBABLY MORE UH THE THING YOU'VE BEEN USING SO FAR IS THAT IT'S NOT UM QUITE RELATED TO LOCATION SO YOU CAN HAVE UM UH FOR ONE PERSON YOU CAN HAVE UH A HIGH ENERGY SIGNAL WHICH IS DIFFICULT TO LOCATE AND VISA VERSA UM I THINK IN YOUR CASE YOU'RE QUITE INTERESTED IN THE LOCATION SO I WOULD ADVISE TO UM USE MORE UH PHASE DOMAIN METHODS SO PHASE UM ONCE YOU HAVE THE FIFTY FOR EACH FREQUENCY YOU HAVE UH THE MAGNITUDE AND THE PHASE SO YOU CAN COMPARE THE PHASE OF THE MICROPHONES AND THIS IS DIRECTLY LINKED TO THE DIRECTION OF THE PERSON UM SO THE MAGNITUDE YOU DON'T REALLY USE OR AND FOR THIS UH I KNOW IT'S A BIT COUNTERINTUITIVE BUT UM MAYBE I'LL GO T TO THE BOARD YEAH OKAY WE MORE USED THE FACE THAN THE MAGNITUDE SO UH I GUESS THE BUT HERE IT'S RELATIVE PHASE BETWEEN THE MICROPHONES SO UH I'LL JUST TRY TO UM S SUMMARISE NO THAT'S WHAT I DON'T DO UM THIS WOULD BE VALID FOR UH LIKE SPEECH RECOGNITION WITH A SINGLE CHANNEL THAT WOULD BE A VERY GOOD IDEA BUT IF YOU CHOOSE TO USE MULTIPLE CHANNELS UM LET'S SAY YOU HAVE FOUR OF THEM WITH F. F. T. YOU CAN JUST UH AS I SAID L LOOK AT THE PHASE BETWEEN THE MICROPHONES AT A GIVEN FREQUENCY SO THE PHASE THE TIME BETWEEN UH AUDIO SIGNALS FOR UM FOR THIS MICROPHONE ON THIS MICROPH OKAY UH BASICALLY THE TIME YOU'RE MENTIONING IS THIS VALUE AND THIS IS THE FREQUENCY AND THIS IS AN ANGLE IN RADIANS MM-HMM OKAY SO UH F. F. T. GIVES YOU A MEASURE OF THESE VALUES UH THIS I CAN POINT TO A PAPER UH I DON'T THINK THIS IS THE RIGHT PLACE TO EXPLAIN EVERYTHING BUT WILL GIVE YOU IF YOU LOOK AT YOUR TABLE UH WHAT WE HAVE DEVELOPED HERE IS AN APPROACH WHERE UM TH UH YOU DIVIDE THE SPACE IN SECTORS MM-HMM FOR EXAMPLE TEN SECTORS AROUND THE TABLE AND UH IN EACH SECTOR YOU WILL GET THE VALUE IT'S RELATED TO THE NUMBER OF MICROPHONES AROUND NO IT'S UH APPLICATION DEPENDENT FOR EXAMPLE UM WELL THIS IS NOT AND ON THIS ONE YOU COULD HAVE A LARGE VALUE ON ON ON THOSE ONES SMALL VALUES UH THESE ARE NUMBER FREQUENCIES WHICH WE ESTIMATE WITH SEVERAL STEPS FROM THESE MEASURES SO TO CONCLUDE WHAT WE ARE DOING IS UH WE ESTIMATE HOW MUCH OF THE FREQUENCY SPECTRUM YOU ARE OCCUPYING WHEN YOU SPEAK OR I AM OCCUPYING IN THIS DIRECTION WHEN I SPEAK AND IT TURNS OUT THAT UM THIS IS QUITE GOOD TO DO UH DETECTION AND LOCALIZATION AT THE SAME TIME MM-HMM MM-HMM OKAY THIS IS A MEASURE OF THE ACTIVITY O OKAY UM