I like da bepis made with real sugar

>>13094 I stopped drinking cokes years ago but Barq's was always my favorite.

Delicious.

>>13094 im sippin on some A 'n' dubya

>>13126 really, a scientist's fuel its my favorit 2! esspecialy the vanilla float flavour

>>11963 I pronounce V's as W's

>>11979 >Alogia Not sure of i have this or just a very low verbal iq (even though i speak 3 languages "fluently")

personally i don't know if i have a stand alone speech disorder but autism + adhd give me a hard time finding words, pronouncing them right, not stammering and then getting the whole sentence out. more often than not i fuck up one word and then have to restart the entire sentence over and over until i get it out one time fully start to finish without fucking it up

I have a British accent In all seriousness though, I fumble and mutter a lot in casual exchanges, especially in my mother tongue. I can roughly tell when a social cue is dropped, just can't grasp the words and cultural sentiment to respond to it properly. It is easier for me to react to such cues in English, but even then I'm not what you'd call "socially fluent". It's a bit uncanny because this isn't a case of being raised multiculturally, by migrant parents in an English-speaking country, or what have you. I just taught myself English from a very early age, and I do mean like from 7 or 8 yrs old (TV and internet, of course). In spite of the aforementioned, I can project my voice clearly during presentations no matter the language, so it only applies to casual situations. I don't think this is considered a speech impediment as such, but figured some hikarins might relate to this. The notion of finding it easier to communicate certain things in acquired languages is understood and not uncommon. I just think my case leans toward the more extreme end where my native fluency is effectively eroded.

>>13325 I gross at 90% of standalone expressions in my native language, like what do you mean "i love you" , bitch you sound like a hog in heat

>>13243 Just ignore the roodey p**s or mock them

wat if raymoo was r baby in th game?

why is my wife the bg now?

>>13326 your wife? but shes with me right now

>>13327 your wife?? that's my wife! get your own!!

could be worse

Probably not

No, only South American

>>10696 Looks like melted popcorn

It probably tastes quite good but the presentation could use some work.

Did you melt chees and eggs

These look like the type of eggs to give me diarrhea for 3 days

>>13317 Has this happened to you before?

>>13298

>>13302 What did I say? What did I do wrong?

>>13305 Lust is Evil

>>13307 How can it be evil when I love it so much

>>13307 nyagan everyone here literally lusts over cvnny doe

>>13299 Haxxor

>>13303 Koshi is vulnerable to time travelers

OP spent so many years without handholding!

php hax?

>>13313 Hikari3 is nodejs not php

(continued from previous post) One option would be to somehow modify the LPC inverse filtering process to be amenable to a parameter configuration instead. However, I am not sure how to do this, or whether it is even possible (perhaps a good place to start would be to frame the problem as a matrix equation?). On the other hand, another option would be to create a cascade filter from the estimated parallel filter. This could be done by first starting with just the first resonance. This is the same in both cases. Then we go on to add the second one. Before adding it, we calculate the current value in the cascade filter at the next resonance's center frequency, we then scale its amplitude by the reciprocal of this value. This new filter will not be exactly the same, but it should be reasonably close. Actually, it may be a better model of the vocal tract. Perhaps also the FRIR algorithm could be used to refine it, even in the cascade case, since it should have a very low error? The final procedure then, would be to run one iteration of the WBHSM algorithm, to estimate the EpR parameters. Then, for each instant an EpR model has been estimated, only the poles are extracted and are then converted to a cascade filter. Next, the inverse filtering is performed. For each inverse filtering frame (which can be as small as a single frame), the closest vocal tract filter estimates are interpolated using, for example, linear or spline interpolation. Next, the z-transfer function denominator of the resultant filter is expanded using the standard procedure to obtain the LPC coefficient, which are finally used to do the inverse filtering itself. The final step is the second pass of WBHSM, over the inversecfiltered signal. Since it has been inverse filtered, there should be no interference between voice pulses. An additional potential benefit is the harmonic envelope no longer contains resonances, which may improve the estimation of the source curve and zeros, although the same effect could also be achieved in the traditional EpR estimation by using two passes. Finally, a combined EpR model is created from the resonance plus the source and residual model obtained in the last step. This procedure makes one significant presumption, which is that most of the error of WBHSM is concentrated in the non-resonance part of the EpR model. This would seem to be a reasonable assumption to make, given that EpR is fitted using multiple harmonics, and that it could furthermore be filtered across multiple frame using techniques such as lowpass filtering, moving averages, or exponential moving averages. However, I do not yet have empirical evidence for this. One possible case wherein the presumption would not hold would be if the error changes slowly over frequency, for example. Actually, a type of error with precisely this fingerprint does exist; it results from the fundamental frequency changing. However, I have already proposed a solution to it, within the RVPM framework: https://listserv.cuit.columbia.edu/scripts/wa.exe?A2=MUSIC-DSP;31999a11.2608A&S=

There's been a lot of mistakes I've known about for a while but haven't bothered to publicly mention yet. I thought I'd finally do that. https://queuesevenm.wordpress.com/2026/10/18/some-overdue-corrections/ Also, another thing I said that was wrong originally is which processing techniques VOCALOID 1 and VOCALOID 2 use. The correct answer is V1 uses Spectral Modeling Synthesis, while V2 uses Spectral Peak Processing/Scaling/Phase-locked Vocoder.

Analysis/synthesis is a paradigm of audio processing. In this paradigm, an audio signal is analyzed to produce a model of the signal. Then this model is transformed according to our wishes. Finally, the modified model is synthesized to produce the transformed signal. This paradigm can be used not only for transforming signals, but also for synthesizing them from scratch by means of selecting prerecorded samples from a database. Also, another method is omit the analysis stage and generate the model using statistics or neural networks. In analysis/synthesis, for any real (i.e. non-synthetic) signal, there will always be a portion of it which cannot be represented well by the model. The exact composition of this component depends on exactly what the model itself models, but in this post I will be focusing on what can not be modeled by any model, that is, stochastic components. Broadly, even if they are not specifically implemented this way, analysis/synthesis techniques that consider the stochastic part due so by first separating out the part that cannot be modeled by the model. Although the remaining component is stochastic, it is not strictly random. Usually, this means it is either tied to the model, or a separate model that describes a non-random aspect of the stochastic component. For example, a typical case is that the spectrum of then stochastic part is not flat as it would be in white noise, but follows an envelope. In that case, the extraction of just the random part would be done by inverse filtering the stochastic component by that envelope, a process known as spectral whitening. From there, there are basically two ways to proceed. Either the random signal is preserved, or it is discarded and replaced with white noise at synthesis. The former has the advantage that, depending how the rest of system is implemented, it may be possible to reconstruct the original signal perfectly when no transforms are applied. Additionally, in practice, the 'random' component will in practice always include some amount of signal, so discarding loses some aspect of the original signal. On the other hand, the latter has the advantage of flexibility. Often, transforming the model requires that we now have more or less samples of the random part. One option would be to interpolate the random component. However, when more samples are required than there were originally (i.e. upsampling), the new signal is smoother than the original. This can be a significant problem, for example if we are slowing down a signal by large factor. From signal theory, we know that all other interpolation methods approximate the ideal sinc-convolution interpolation. In the frequency-domain, that method corresponds to there being no new spectral components above the old Nyquist frequency when upsampling. Given that our random component is supposed to be white noise, i.e. that it should have a flat spectral envelope, we can now see where the smoothness problem comes from. Thus, from this observation, I propose a hybrid model. We preserve the original random component, with all the advantages that brings. When requiring a different amount of samples than there were originally, we resample it. However, in the case of upsampling, we replace the new spectral bins with random values instead of zeros. (continued in next post)

(continued from previous post) Thusfar, I have discussed how the spectrum should change under this proposal, however in practice the direct principle cannot be implemented directly. Two practical reasons are that my FFT implementation has fixed maximum size, so it cannot be used on arbitrary-length signals; and that doing so also becomes inefficient with larger and larger FFT size. However, a more fundamental issue is the circular nature of the FFT which results in artifacts whenever the signal is not entirely periodic with a period corresponding to exactly the FFT window. This problem can be either mitigated through techniques such as overlap-and-add; or time-domain convolution (i.e. filtering) can be used instead. For linear transforms, the latter is almost always used because it is much more efficient, and at the same time, it produces far lower levels of artifacts. Based on that, this proposal could be implemented by lowpass filtering the interpolated signal (i.e. resampling), and adding to that highpass-filtered white noise whose bandwidth corresponds to the difference between the new and old sampling rates. In practice, real lowpass filters are not ideal, so they have a non-zero transition band. Thus, the resulting envelope is not completely flat and would have spikes due to the transition bands. This could be reduced, although not completely, by increasing the filter size, at the cost of additional computational complexity. Another option is to adjust the bandwidths slightly such that total distortion is minimal (i.e. error can in either direction, not just positive). Another interesting idea would be to see if a window could be used whose frequency response is overlapping. In the time-domain, some windows are said to be overlapping, for example the triangular window, Hanning window, and Gaussian window. What that means is that it is possible to separate the windows by a specific distance, and the resulting of adding them together is one outside of given edges. The Kaiser-Bessel window is almost-but-not-quite overlapping, which is because it is almost-but-not-quite the Gaussian window. If a window's frequency response were to be overlapping, using it as the FIR filter construction window would solve the problem from before. Special care would need to made to ensure that the filter's transition band matches the size needing for proper overlapping as closely as possible. Usually we do not care about this since the transition band being smaller than desired is usually exclusively a good thing.

>>13286 Actually, thinking about it, the resilient border interpolation at synthesis is not unnecessary now given by proposed WBHSM+LPC framework. The reason it became unnecessary before is that the proposed RVPM modeled each voice pulse as its full impulse response, assuming a time-invariant filter for each pulse (although I suppose the EpR differential envelope may model some of the time-variance of the filter, but this breaks as transforms are applied). The new WBHSM+LPC model is a literal combination of WBHSM and LPC where WBHSM models the source signal. Since that signal is not filtered, each voice pulse is actually independent, and pulses and periods are truly equivalent. Because of that, we actually go back to modeling each individual pitch period, reintroducing the need for the synthesis border interpolation. For this, we can do the same thing we do in the analysis stage where we shifted the window such that the window starts coincides roughly with the glottal opening instant and only the right side of the window is interpolated. This becomes especially beneficial for the WBHSM+LPC synthesis since the left side is where the voice pulse actually is, while on the right side the glottis should be closed or almost closed.

who cares. worst vocaloid and adopted by newfags. Luka will always be on top

>>12276 Nuh uh

>>12277 Yeah huh (　´ω｀)

>>12276 Not vocaloid though