search for: snr

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 inverse=1,snr = 90.826012 nfft=36 inverse=0,snr = 87.883324 nfft=36 inverse=1,snr = 88.161852 nfft=50 inverse=0,snr = 84.841953 nfft=50 inv...

...t;> 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 inverse=1,snr = 90.826012 >> nfft=36 inverse=0,snr = 87.883324 >> nfft=36 inverse=...

...n 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 = 96.285703 nfft=256 inverse=0,snr = 86.726515 nfft=256 inverse=1,snr = 87.308386 nfft=512 inverse=0,snr = 82.577935 nfft=512 inverse=1,snr = 87.103849 nfft=1024 inverse=0,snr = 84.950837 nfft=1024 inverse=1,snr = 87.105776 nfft=2048 inverse=0,snr = 84.749139 nfft=...

Hello Jean-Marc, **Resending.. not sure why subject got removed earlier** 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 inverse=1,snr = 90.826012 nfft=36 inverse=0,snr = 87.883324 nfft=36 inverse=1,snr = 88.161852 nfft=50 inverse=0,snr = 84.841953 nfft=50 inv...

...Viswanath Puttagunta wrote: > 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 inverse=1,snr = 90.826012 > nfft=36 inverse=0,snr = 87.883324 > nfft=36 inverse=1,snr = 88.161852 > nfft=...

...692195957eb66989622c7b1ad8448b06 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 = 96.285703 > nfft=256 inverse=0,snr = 86.726515 > nfft=256 inverse=1,snr = 87.308386 > nfft=512 inverse=0,snr = 82.577935 > nfft=512 inverse=1,snr = 87.103849 > nfft=1024 inverse=0,snr = 84.950837 > nfft=1024 inverse=1,snr = 87.105776 > n...

...K=6... Testing CWRS with N=14, K=7... Testing CWRS with N=14, K=8... Testing CWRS with N=14, K=9... Testing CWRS with N=14, K=10... Testing CWRS with N=14, K=11... Testing CWRS with N=14, K=12... Testing CWRS with N=14, K=13... PASS: cwrs32-test nfft=32 inverse=0,snr = 150.247514 nfft=32 inverse=1,snr = 149.620755 nfft=128 inverse=0,snr = 145.260928 nfft=128 inverse=1,snr = 144.515158 nfft=256 inverse=0,snr = 144.879667 nfft=256 inverse=1,snr = 144.452727 nfft=36 inverse=0,snr = 144.039051 nfft=36 inverse=1,snr = 144.179535 nfft=...

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++) { enoise+=(bak2[i]-input[i])*(bak2[i]-input[i]); esig += bak2[i]*bak2[i]; }...

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 doesn't match the results you reported on this list in May, where the FFTs appeared to pass tests, but the MDCTs did no...

Actually, the SNR calculation in testenc has been broken for a while. The reason for taking the last frame was that the codec would introduce a one-frame latency. I changed that to half a frame (10 ms) a while ago and never updated testenc. Jean-Marc Le lun 01/09/2003 à 13:12, Mike Dunn a écrit : > Hi a...

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 thought I had attached that. Anyway: anova.glm(aov( nFD~Type+size+Modality+Error(Snr/(Type+size+Modality))), data=kk) # Same m...

...n their help package, they have listed commands that will allow a sample runthrough of their algorithm using a sample dataset. I entered these codes in but was returned the following error messages: data(exampleMS) Warning message: In data(exampleMS) : data set 'exampleMS' not found > SNR.Th <- 3 > peakInfo <- peakDetectionCWT(exampleMS, SNR.Th=SNR.Th) Error: could not find function "peakDetectionCWT" > majorPeakInfo = peakInfo$majorPeakInfo Error: object "peakInfo" not found > peakIndex <- majorPeakInfo$peakIndex Error: object "majorPeakIn...

...st 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 19.538232 2 HALAF032 A L A F 2 2 -32.64881 86.22040 -23.31558 -9.333224 3 HALAF032 A L A F 2 4 -18.91058 42.12168 -35.60250 16.691919 4 HALAF032 A L A F 2 6...

...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 new project -- only to have your contributions ignored. </RANT> <p>> Actually, the SNR calculation in testenc has been broken for a while. > The reason for taking the last frame was that the codec would introduce > a one-frame latency. I changed that to half a frame (10 ms) a while ago > and never updated testenc. > > Jean-Marc > -------------- next part ----------...

...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, factor(t$snr), summary)) would do something, but it doesn't. Say my data is (for example) SNR timeError 4 1.3 4 2.1 4 1.2 6 2.1 6 2.2 6 2.1 8 3.2 8 3.7 8 3.1 I want to produce a plot of SNR vs mean(timeError) with error bars of magnitude 3 sigma. here's what I've got so far (without the error bar...

...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 while using a low SNR, because a low SNR will lead to a large error-vector which increases energy because of the orthogonality property. |signal+error| ==> -------------- ~= sqrt(1+exp(-SNR*c+d) > 1 |signal| (again, this is the average case. 'c' should be around 8.6 and 'd' around 0.0...

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 c...

...gt; Hello, > > I'm trying to understand how to interpret the differences in results between two versions of a 2-factor ANOVA with (slightly?) different models, of an observable y, a within-subject factor 'indep' and a grouping factor 'cond' (and a subject 'factor' Snr): > > >>summary( aov( y~cond + indep + Error(Snr/indep) ) ) > > # example results: > Error: Snr > Df Sum Sq Mean Sq F value Pr(>F) > cond 1 103.1 103.1 1.425 0.248 > indep 5 159.8 32.0 0.442 0.813 > Residuals 18 1301.6 72.3...

...is (input_length)*(sample_target)/(sample_source) Use one long double iFFT for the entire thing. We'll call the FFT and iFFT of this our reference. Then, for each resampler below, I've reported the maximum numerical difference in the time domain(comparing ref[i] with sig[i]) as well as SNR. Since my knowledge of SNR for this is a bit sketchy, it's computed as follows: Pad resampled signal with as many zeroes as there are samples. Convert to long double. Use one long double FFT for the entire thing. Then, for both reference and resampled, let power[i] = sqrt(real[i]^2 + imag[i...

Hi, This question is related to the bwplot issue I reported yesterday. I have a 3 factors (2x3x2) dataset that I collapsed into a 2 factors dataset (3x2 = sizexModality). For size==small, I have 2 observations per subject (Snr), for the other sizes only 1. I reckoned that aov (and underneath, lm) might handle this as it should, since the subjects are idendified, when I do > aov( y~Modality*size + Error(Snr/(Modality*size)) ) I indeed get a result without any warning. Remembering a remark on unbalanced designs in ao...