similar to: Fw: Results of Automated Batch Tests

I realize that hearing tests are far superior for calibrating codec internals, particularly the perceptual enhancement. The tester is for finding gross mistakes like overflows. The test program includes code to subsample the 32Khz source down to 8 and 18 Khz. I've broadend the tests to try all the possibilities like VBR, perceptual enhancement, complexity and quality. The results are

Sounds like a great idea, except for the part about only running on linux/cygwin. What would you need in order to make it cross-platform, as libspeex is? "Nic Roets" <speex@rational.co.za> wrote: > > I'm working on a patch that will batch test most of the code in > libspeex. Here's the process (Feel free to suggest changes) : > 1. Download the

Hello Jean-Marc, Below are the results that show test_unit_dft passes, but test_unit_mdct fails (only for nfft=480, 960, 1920) Note: Tested on BeagleboneBlack(Cortex-A8) fixed point on branch [1] ./test_unit_dft nfft=32 inverse=0,snr = 88.394372 nfft=32 inverse=1,snr = 93.896470 nfft=128 inverse=0,snr = 89.185895 nfft=128 inverse=1,snr = 93.537021 nfft=256 inverse=0,snr = 88.353151 nfft=256

Hi, Apologies if this is a known issue, but running make on revision e3187444692195957eb66989622c7b1ad8448b06 fails one of the tests when using fixed point configuration (floating point is ok) on my linux x86. Note that libopus1.1, as extracted from the tar ball, is OK. Specifically, the tests that fail are in celt/tests/test_unit_mdct: nfft=32 inverse=0,snr = 85.341197 nfft=32 inverse=1,snr =

Hello Jean-Marc, Yep, that was it.. with your patch, test_unit_mdct passes for all nfft. So, what you do you suggest the next step here is? Regards, Vish On 8 May 2015 at 12:30, Jean-Marc Valin <jmvalin at jmvalin.ca> wrote: > Hi, > > Can you apply this change to the MDCT test and run it again. See if more > (all) sizes pass. Given the results, I strongly suspect an

Hi Folks, I have just started using dplyr and could use some help getting unstuck. It could well be that dplyr is not the package to be using, but let me just pose the question and seek your advice. Here is my basic data frame. head(h) subject ageGrp ear hearingGrp sex freq L2 Ldp Phidp NF SNR 1 HALAF032 A L A F 2 0 -23.54459 55.56005 -43.08282

I'm trying to get these cleaned up and landed, but I'm running into some trouble with this patch. Using commit a08b29d88e3c (July 21) of Ne10, I'm seeing test failures for 60-point FFTs: nfft=60 inverse=0,snr = -3.312408 ** poor snr: -3.312408 ** nfft=60 inverse=1,snr = -16.079597 ** poor snr: -16.079597 ** All other sizes tested appear to work fine (84 to 140 dB of SNR). This

Concerns: R 1.5.0 gui version, Windows (downloaded binary) and Linux (installed from sources). # Load the data from the attached file: kk<-read.table("__filename__", header=1) # attach the data: attach(kk) Snr<-factor(Snr) # fool around with a call to anova.glm(): anova.glm( aov( nFD~Type+size+Modality+Error(Snr/(Type+size+Modality)) ) ) # Error: object nFD not found # Well, I

I found a package on www.bioconductor.com that allows me to install using this line: source("http://bioconductor.org/biocLite.R") biocLite("MassSpecWavelet") The prompt showed me the following message: Running biocinstall version 2.1.10 with R version 2.6.2 Your version of R requires version 2.1 of Bioconductor. trying URL

? Do you have any references for me to investigate, I am trying to understand how noise is reduced by introducing higher sampling rates. (I tried to search, but maybe it is so obvious that nobody even explains it) This is not very obvious. It requires you to understand basic signal processing theory. I will give some pointers below. Any physical signal (e.g. audio coming out of speaker, current

Hi all, I'm new to the group. I'm looking at the speex code with an eye towards maybe helping out with either codec optimization or fixed-point implementation, The SNR calculation in testenc.c and testenc_uwb.c doesn't make sense to me. The code is { float enoise=0, esig=0, snr; for (i=0;i<FRAME_SIZE;i++) {

Hi, I'd like to use R to do what excel pivot tables do, and plot results. I've never used R before, and I've managed to do something, but it's quite a lot of code to do something simple. I can't help but think I'm not "Doing it the R way". I could be using R for the wrong thing, in which case, please tell me off. I was hoping something like plot(by(t,

I've had slew of problems with my Bell Canada Single Number Reach (SNR) dropping in the past couple of months. Another outage Monday for several hours has me wondering if there's a way to 1. Make a call out of my system via a PSTN back to my SNR line, say every 30 minutes (this I'm sure is easy enough via the call file...however...) 2. Track the outgoing call and match to an

We've made a little change that changed the output of vorbis is there a way to check the accuracy of the output against the original output? I also asked this question in the irc ... Thanks, Tal.

>> Yes, I plan to use it in a VoIP environment if I can get latency reduced to >> an acceptable level :) >> The latency depends directly on the overlap parameter, which also controls >> the quality. Higher quality => higher latency. You could set the overlap to >> 0, but that would give you some nasty artifacts. >> You can also resample with smaller block

I submitted a version of testenc.c to this list MONTHS ago that fixed this problem. It uses a reconfigurable group delay. If you had given me cvs access I could've committed the changes myself. Anyway, the file is attached (again). Maybe this time you'll actually notice the email. Sorry if I seem a little short. It's just very annoying to put the time into learning and improving a

I was wondering if the developers were using anything to "objectively" test the quality of the speex vocoder. For instance PSQM or one of the many derivatives. Mean Opinion Scoring seems an expensive route. Is there some open source software to use for this? <p>--- >8 ---- List archives: http://www.xiph.org/archives/ Ogg project homepage: http://www.xiph.org/ogg/ To

Hi, there ! I'm pretty sure that the following text (and attached picture) explains the reason for the gain of high frequencies. when quantizing a vector scalar by scalar by simply rounding each scalar to the nearest level, the quantization-error-vector and the original signal-vector can be assumed to be orthogonal (average case) This is a problem when we want to preserve the energy level

I have 2 tables,T1 and T2. There are 4 columns of T1 and 2 columns of T2 T2 1. 990334. G000021 2. 844047. G000025 3. 281739. G000037 4 280903. G002615 5 418546. G004657 The data in T1 V1 V2 V3 V4 1 T00015 AFP1 human G004657 2 T00035 AP-2alphaA human G002615 3 T00036 AP-4 human G003918 4 T00040 AR human G004953 5 T00045

Dear R-List, I would like to recode categorial variables into binary data, so that all values above median are coded 1 and all values below 0, separating each var into two equally large groups (e.g. good performers = 0 vs. bad performers =1). I have not succeeded so far in finding a nice solution to do that in R. I thought there might be a better way than ordering each column and recoding the