YOU JUST NEED TO TAKE THE ERROR OF N UH RESIDUAL OF THIS FULL WINDOW AND THEN RESIDUAL OF ENERGY OF THE SUB WINDOWS AND THEN YOU CAN JUST DIVIDE THEM AND THEN THE ONLY THING IS LIKE AGAIN HE HAS TO USE SOME THRESHOLD TO DECIDE THAT THAT'S ONLY THE P PROBLEM HE GOT UH HE'S FOUNDING SOMETHING MORE UH AROUND THREE OR THREE POINT FIVE E. S. T. OPTIMUM THRESHOLD BUT THE AND ANOTHER ADVANTAGE IS LIKE IT'S NOT REALLY CHANGING SPEECH RECOGNITION WHATEVER SO NOT REALLY CHANGING BECAUSE OF THE THRESHOLD SO UH YOU DON'T REALLY NEED TO F LIKE FIDDLE WITH THRESHOLD A LOT SO MM YEAH BUT WHY DO THE MAIN VALUE WHICH ARE COMPARING TO THE THE THE OVERALL VALUE OR OVERALL LIKELIHOOD OR WHATEVER YOU CALL IT WHY HE'S USING THE WHOLE SIGNAL FOR THIS BECAUSE SO IF THE WHOLE SIGNAL WAS UH STEADY SOMEHOW AND IT IT WOULD BE ABLE TO BE MODELLED BY L. P. MODEL WELL THEN THE RESIDUE WILL BE VERY LOW SO HE'S SUPPOSING THAT THE WHOLE SIGNAL WON'T BE ABLE TO TO BE CAPTURED BY L. P. MODEL AND THAT THE RESIDUE WOULD BE HIGH ENOUGH OR HO WH WHY HE'S COMPARING THE O TO TO THE OVERALL YOU KNOW I I DON'T SEE THE POINT OF DIVIDING OVER THE THE WHOLE STUFF WHAT THEY SAY HERE IS LIKE SUPPOSE IF YOU ANALYSE TWO DISTINCT OR UH THIS L. P. ANALYSIS IN THE SAME STATIONARY ANALYSIS WINDOW THE CODING WILL ALWAYS BE GREATER THAN THE ONES RESULTING FROM THIS ANALYSIS IN TWO WINDOWS TWO STATIONARY WINDOWS Y WELL SOUNDS SOUNDS REASONABLE MM-HMM I SEE I SEE I SEE WELL IT WILL BE ALWAYS GREATER THAN THE MM TH THESE ERRORS OF TWO STATIONARY WINDOWS BECAUSE ACTUALLY IF IT IF IF UH THE WHOLE SIGNAL WOULD BE ABLE TO BE MODELLED BY L. UH BY L. P. C. THEN ANYWAY HE HAS TO DESIGN SOME SOME FRAMING SO EVEN IF IT'S VERY STATIONARY BY D UM BY DIVIDING OVER THIS WHOLE STUFF HE IS ABLE TO FIND SOME REASONABLE BOUNDARIES UM M MA MAKES SENSE UM YEAH THIS MAYBE I THINK YEAH THIS HE'S GETTING SOME IMPROVEMENT BUT HOW DID YOU COPE WITH WHAT YOU MENTIONED SOME TIME BEFORE THAT IF IT'S A VERY LONG SESSION FOR EACH TWELVE POINT FIVE FRAME YOU WILL ACTUALLY GET THE SAME SEGMENT NOW SO HOW D AH OKAY THAT HAS TO BE POWER NOT ENERGY DEFINITELY NO ENERGY LIKE BECAUSE C. GYRO COMPONENT LIKE SO HE'S USING M. F. C. C. SO HE'S NORMALISING THE C. GYRO COMPONENT FOR THIS T YEAH THEN POWER INSTEAD OF ENERGY ENERGY OVER LENGTH IS POWER IT'S OKAY LIKE SAMPLES OR YOU CAN SEE ROOT MEAN SQUARE ENERGY OR SOMETHING YEAH YEAH DUH DUH DUH DUH DUH OKAY SO BUT THIS SO TWO THINGS I STILL DON'T UNDERSTAND THI THIS IS SUPPOSED TO BE ONLY FOR M. F. C. C. S AND THESE EASY FEATURES AH ONE QUESTION I I WANTED TO ASK BEFORE DOES DOES HE PRESERVE WITHIN THE FEATURE VECTOR THE FRAMING HE'S USING FINALLY BECAUSE DOES HE SOMEHOW PUT THE INFORMATION ABOUT WAS THE SIZE ABOUT THE FRAME TO THE FEATURE VECTOR ITSELF OR HE'S THROWING AWAY THIS FRAMING NO N NO YOU VO WHAT YOU MEAN LIKE THE WINDOW LENT THE SIXTY MIL NO IT I IT DOESN'T REALLY UH BUT I THINK IT'S HE HE DOESN'T USE I GUESS BECAUSE UH UH THEN LIKE YOU JUST YOU TAKE THESE FEATURES AND YOU TRAIN MODEL SO IT REALLY MATTERS WH WHAT FRAME SHIFT YOU'RE USING FOR MODELS LIKE BECAUSE HOW MANY HE NEEDS TO USE SAME FREQ NO NO WAIT NO HE WILL GET LIKE EVERY TEN MILLISECONDS HE GET ONE FRA ONE FEATURES LIKE HE IT DOESN'T REALLY DEPEND BECAUSE SUPPOSE IF YOU TAKE YOUR T CASE LIKE YOU YOUR WINDOW I MAYBE LONGER SO DOESN'T REALLY MATTER LIKE HOW WHAT SIZE WINDOW YOU USE OR NOT BUT I TELL UH AT EVERY TEN MILLISECONDS WHETHER YOU GIVE SOME FEATURES OR NOT REALLY MATTERS NO LIKE SO SO IT DOE YOU CAN USE UH THIRTY MILLISECONDS OR FIFTY MILLISECOND WINDOW OH PROBABLY I DIDN'T GET IT TOO SO THE FRAMING IS ACTUALLY EQUI DISTANT JUST THE WINDOWS ARE WIDER OH AH I SEE BECAUSE IT WOULD BE CRAZY YEAH A LITTLE BIT YEAH IT MIGHT BE DIFFICULT OH I SEE SO SO IT'S IT'S LIKE THIS IT'S NOT ONLY WITH THIS MODEL IT'S IN SPECTRA AND SHIFTS IN DELTAS BECAUSE YEAH BUT IT'S REALLY COMPLICATED I THINK IF YOU USE YEAH THAT BECOMES REALLY YEAH BUT THIS IS KIND OF INTERESTING BUT I SAW THE ALGORITHM I HAVE BEEN USING EVEN THAT CALLED TEMPORAL DECOMPOSITION I DON'T KNOW IF YOU KN YOU KNOW THAT LIKE UH I DON'T KNOW WHO PROPOSE IT I JUST HAD IT FROM FROM BEAMBOT FOR LIKE A BEAMBOT DO YOU KNOW THE GUY HE'S FRENCH UM SOMEWHERE NOW IN TELECONFERENCE LIKE MM I DON'T KNOW WHERE HE'S WORKING BUT TH WE WERE USING IT FOR UH SPEECH CODING SO WE JUST HAD A SPEECH AND YOU HAD DECOMPOSE A SPEECH INTO SUCH SEGMENTS TEMPORAL SEGMENTS WHICH WERE STATIONARY INSIDE AND THEN YOU CAN MORE OR LESS UH QUANTISE THOSE SEGMENTS SOMEHOW AND USE IT FOR ENCODING LET'S SAY OR AND IT WORKED PRETT YEAH YEAH MM THEY WERE USING LIKE S. V. D. STUFF SINGULAR VALUE DECOMPOSITION FOR THAT AND IT WORKED PRETTY WELL LIKE I WAS SURPRISED LIKE YEAH YEAH EXACTLY UH HE WAS USING THAT YEAH SO WHY DON'T YOU STILL USE IT IF IT WORKS SO WELL NOB NOBODY USE IT FOR RECOGNITION STUFF I THINK I NEVER HEARD THAT YEAH BUT YOU DO THE SPEECH CODING SO DO YOU STILL USE IT WHY MAYBE YEAH HE WAS UH QUOTING THAT PAPER ALSO LIKE V HE WAS TELLING THIS UH THIS IS KIND OF OPTIMISATION CRITERIA WHAT THIS TEMPORAL DECOMPOSITION IS DOING LIKE YOU'RE TRYING TO SEGMENT YOUR S SIGNAL INTO LIKE DISCREET WINDOWS AND THEN SO BUT HE WAS TELLING LIKE THIS UH RELATIONSHIP BETWEEN OPTIMATION AND AND THEN CAUTIOUS STATIONALITY IS NOT REALLY OBVIOUS SO STATIONALITY IS AGAIN DIFFERENT NO STATIONALITY IS THIS IS OPTIMATION WE WANT TO LIKE SEE SOME SIGNAL FEW SEGMENTS WHICH CAN REALLY REPRESENT WHOLE SO MAYBE THAT'S WHY LIKE THIS MAY NOT BE REALLY MM-HMM THAT'S TRUE UH IT'S INTERESTING THIS YEAH THIS ONE YEAH HE'S GETTING SOME IMPROVEMENT N UH THIS IS A YEAH WELL THERE SHOULD BE AFTER EACH CONFERENCE THERE SHOULD BE SOMETHING LIKE TEN PEOPLE SHOULD SIT DOWN READ THE PAPERS AND THEN RANK THEM YOU KNOW A ALL THE ALL THE ALL THE SIMILAR PAPERS WITHIN ONE SESSION SAY SP UH FOCUS TO THIS TOPIC AND SAY THIS IS THE BEST THIS WORKS SLIGHTLY WORSE THAN THIS ONE AND THROW AWAY WHAT IS NOT THAT GOOD AND AND TH THE FIRST THREE THREE PAPERS WHICH ARE THE BEST SHOU SHOULD BE IMPLEMENTED AND USED FROM FROM THAT TIME ONWARDS