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S.A.R Labs ??????????????? - Amplifiers/Preamplifiers ...
https://www.canuckaudiomart.com/forum/viewtopic.php?t=9738
Anyone ever owned any Scientific Audio Research Labs products before? I just acquired a SAR Labs MOS 450 power amp and have installed it in my main rig in place of my Sonic Frontiers power amp and paired up with my SFL-2, driving a pair of Logan CLSes. Not 10 days into the ownership I am simply floored by how well this amp sounds driving my Logans.
SAR Scientific Audio Research Mosfet 450 Hidden Canadian ...
https://www.audiogon.com/listings/solid-state-sar-scientific-audio-research-mosfet-450-hidden-canadian-gem-2013-07-18-amplifiers-canada
SAR Scientific Audio Research Mosfet 450 Hidden Canadian Gem! [Expired] "Up for sale is the spectacular "MOS 450" 225 watt per channel stereo power amplifier handbuilt in Canada by Scientific Audio Research Labs (S.A.R. Labs) You'll have to ask around the audiophile community about S.A.R. Labs because it will be unlikely you'll ever find an ...
Audio Research Labs – Audio Research Labs
https://www.audioresearchlabs.com/audio-research-labs/
Dr. Quackenbush brings years of experience in research and technology development, including. More than 2 years as founder and Vice President of Audio Technology at Lightspeed Audio Labs. More than 15 years as Member of Technical Staff at AT&T Bell Laboratories. More than 22 years of participating in ISO/IEC standardization and more than 20 ...
Audio Research Labs – Speech and Audio Technology
https://www.audioresearchlabs.com/
About Dr. Schuyler Quackenbush. Dr. Schuyler Quackenbush founded Audio Research Labs in 2002, where he is CEO and principal consultant. Dr. Quackenbush brings years of experience in research and technology development, including. More than 2 years as founder and Vice President of Audio Technology at Lightspeed Audio Labs.
Audio Analytics - Microsoft Research
https://www.microsoft.com/en-us/research/project/audio-analytics/
Audio events detection was an early project in our audio understanding research. Using pre-trained CNN models as feature extractors, we enable knowledge-transfer from other data domains, which can significantly enrich the in-domain feature representation and separability.
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