similar to: Opus vs AAC (endurance test)

Opus is specifically designed to survive tandeming but you need to keep the frames aligned and not mess with the gain, which your tools probably do not do. On Thu, Nov 16, 2017 at 10:58 PM, encrupted anonymous < sergeinakamoto at gmail.com> wrote: > using iTunes i've noticed that AAC is > very good at re-encoding own lossy sound. > let's test Opus! > > neroaacenc.exe

Good morning. I've ran a test against MP3 format. Code: (first convert tested audio file to 16 bit 48khz with sox.exe if needed) lame.exe -b 320 48khzfilein.wav -o fileout.mp3 lame --decode fileout.mp3 -o fileout.mp3.wav opusenc.exe --bitrate 320 48khzfilein.wav fileout.opus opusdec.exe fileout.opus fileout.opus.wav wavdiff.exe 48khzfilein.wav fileout.mp3.wav -diff fileout.mp3.delta.wav

Jean-Mark sarkasm. Jean-Markasm. (Bonus points for providing an actual noisy WAV! ^_^) On 30/10/2017 20:28, Jean-Marc Valin wrote: Hi, Before I comment on the graphics you posted to visualize the difference between two audio signals, I'd like to ask for your help in evaluating my JPEG encoder. I've encoded an image with JPEG and then computed the difference with the original. I then

Hi, Before I comment on the graphics you posted to visualize the difference between two audio signals, I'd like to ask for your help in evaluating my JPEG encoder. I've encoded an image with JPEG and then computed the difference with the original. I then converted the difference to sound. You can listen to the image difference on this clip: https://jmvalin.ca/misc_stuff/diff.wav Can you

Just to be clear, my goal here wasn't to make fun of anyone, but to drive the point that spectrograms should *never* be used to demonstrate quality. The only case where they can sometimes be useful is for diagnostic purposes. If you hear something and you're not sure what you're hearing exactly, then sometimes a spectrogram can help you figure out what it is. That's pretty much it.

Hi guys, as MP3 and Opus have very similar objectives, I think the original poster's question was a valid one: Why does Opus have more artefacts in the lower frequency ranges than MP3 has? The spontaneous suspect that lower frequency artefacts may be more noticeably than higher frequency artefacts seems plausible, also. Is it a matter of energy (which is higher for higher frequencies)? When

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In my undertstanding of the problem, the model lme1 <- lme(resp~fact1*fact2, random=~1|subj) should be ok, providing that variances are homogenous both between & within subjects. The function will sort out which factors & interactions are to be compared within subjects, & which between subjects. The problem with df's arises (for lme() in nlme, but not in lme4), when

Dear ExpeRts, Suppose I have a typical psychological experiment that is a within-subjects design with multiple crossed variables and a continuous response variable. Subjects are considered a random effect. So I could model > aov1 <- aov(resp~fact1*fact2+Error(subj/(fact1*fact2)) However, this only holds for orthogonal designs with equal numbers of observation and no missing values.