similar to: Self Calling test

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

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, 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 =

network outages and recent tests have shown that works well, albeit the switch takes about 20 minutes to propagate the dns updates but otherwise flawless. =20 It's embarrassing and I'm losing credibility when clients are asking if I'm still in business as the phone has dropped way to often in the past few month. Interesting enough all outages to date have been Fridays or Mondays. =20

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

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++) {

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

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

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,

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

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

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

Hi Jean-Marc > Can you explain what you mean here by "speech switching" By speech switching I mean the adaption of "gain2" when near-end or far-end is talking. What is important is that the timing is good and that the gain is low/high while far/near-end is talking. By timing I mean that the "gain2" should remain low until all far-end talk is final and that the

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 also wondering if there is a standard set of input sequences people are using to test Speex. I haven't stumbled upon it/them yet. > -----Original Message----- > From: owner-speex-dev@xiph.org [mailto:owner-speex-dev@xiph.org]On > Behalf Of Jean-Marc Valin > Sent: Tuesday, February 25, 2003 7:24 PM > To: speex > Subject: Re: [speex-dev] Quality > > > > I

Hi Jean-Marc, I have implemented fixed-point FFT with 32-bit twiddles. Now I want to evaluate the accuracy, what method does Opus use? I use function implemented inside Ne10 to calculate SNR. Any comment? | size | SNR (dB) | | 16 | 82.558587 | | 32 | 83.530298 | | 60 | 80.292433 | | 64 | 82.752950 | | 120 | 79.625077 | | 128 | 83.091260 | | 240 | 79.555263 | | 256 |

Dear R users, I’m trying to understand how correlated predictors impact the Relative Importance measure in Stochastic Boosting Trees (J. Friedman). As Friedman described “ …with single decision trees (referring to Brieman’s CART algorithm), the relative importance measure is augmented by a strategy involving surrogate splits intended to uncover the masking of influential variables by others

> The C5x and C6x output diverges in build 10143, which has log message "lpc > floor converted to fixed-point." Also, the measured SNR changed from 11.05 > in builds 9854-10141 to 9.22 and 9.24 in 10143. Actually, build 10143 introduced another bug, that was the reason for the 1.1.11.1 release. > There is just four lines in modes.c which declare the constant, and one

>> I think the residual echo estimation is fairly reliable but I do not know >> how to use this to improve Pframe and in that way solve our main problem >> with the gain during near end talk. > > How can you tell that the residual echo estimation is reliable? in any > case, I suspect that the whole Pframe idea might have to be revised > (i.e. computing it completely