There's also the adaptive window procedure that wraps the TWM f0 estimation. One thing I had been noticing for a long time was that Kaiser-Bessel beta values about 10% higher than the recommended values given in Cano 1998 seemed to perform much better. I had assumed this was just because of issues with my code, or the audio samples I was testing on. Much later, I was experimenting in python when I noticed a function called kaiser_beta which converted something else abbreviated to 'a' to the equivalent beta value. Previously in Cano 1998 and in other places, I had seen the Kaiser-Bessel parameter as alpha instead of beta. Up until this point, I had either not paid attention to this, or I had assumed that these had referred to the same thing. I did some research and found out that it converts between attenuation and the beta value for the Kaiser-Bessel window. Then I found that there is indeed an alpha form of the parameter and it is not that same as beta. Confusingly however, it is not attenuation, but both abbreviate to the same thing. The Kaiser-Bessel beta can be determined by just multiplying the alpha value by pi. Interestingly, this is much higher than the 10% I tested, however it seemed to perform better (or at least not worse) anyway. A possible explanation for this discrepancy is that the adaptive window is larger than the window I used to test the adjustment originally.

Another improvement relating to windows is the window used for the harmonics that are fed into MFPA. Originally, I had used the same Kaiser-Bessel window for both. I later switched to a Blackman-Harris -92dB window, which I had seen mentioned in the paper. This resulted in a significant improvement. Another improvement I tried was adapting the window size to a value relative to the period of the estimated fundamental frequency. I tried doing this - using the same number of periods as are used for the Kaiser-Bessel window used for TWM - and noted a substantial improvement, even more so than the improvement from switching to the Blackman-Harris window in the first place. Indeed, this matches the results contained in the study. In the WBVPM section, they observe a considerable improvement (up to -10dB) when using an adaptive window size when compared to a fixed window for narrow-band analysis. In that same section, they also found 2 to be the ideal number of periods for minimizing noise and also did experiments with a Hann window. Perhaps experimenting with these ideas could lead to im

Comment too long, view post No.178286 to see the full comment.

START FRAMES

In the audio samples I have provided so far, I have cut off the first part of the audio. The issue is with pitch estimation for early frames. Remember that are analysis window is multiple f0 periods in size. Because of this, it can't fit at the start so it has to be decreased to a much smaller size. This is much more of an issue now that I have decreased the hop size substantially. I have now set it to skip the first few frames, because otherwise, the forced extremely small window size causes the whole pitch estimation system to irreversibly destabilize. I've been thinking of solutions to this problem. One solution could be to let the analysis window take on the full size it wants and pad the area before the start with zeros or maybe something else, this could also be used for the end. Possibly the most promising solution I have come up with, although I have not test any of these, is to back fill the previous the pulses with the first good estimated pulse onset minus integer multiples of the first good estimate of the fundamental frequency. This should work assuming both the first pulse and fundamental frequency estimate are good, the fundamental frequency stays relatively constant over the start section, and the start section only contains a few pulses. Luckly the last criteria will always be satisfied as the size of the start section is half the size of the window, and the number of pulses is then (window_size / period) / 2, but the window size in the adaptive framework is just a small number of periods, so we are left with the (mostly) constant adaptive_period_count / 2 as the number of pulses.

WIDE-BAND VOICE PULSE MODELING

Regarding the patent issue, I have determined that it applies only to the specific technique in Bonada 2008 WBVPM of using periodization to achieve a real-sized discrete fourier transform. However, that section also another option, that being interpolation. I have implementated it and found it to work well. I did a test a found a noise level of about -140dB (for reference, 1ulp for a single-precision float is about -145dB), which is extremely negligible and comparable to the results in the study for the periodization technique. I have also added the ability to use a few extra samples on the side to improve the spline. However, I have not tested the consequences of this variation. I don't know whether the original implementation did something like this.

Text in the patent: "generating for each pulse a sequence of repetitions of said audio pulse, said audio pulse being repeated according to its own characteristic frequency; deriving frequency domain information associated with at least some of the sequences of repetitions of said audio pulses, each said sequences of repetitions of said audio pulse being represented as a vector of sinusoids based on the derived frequency, said vector of sinusoids corresponds to a sinusoidal series expansion of the specific audio pulse;"

Bonada 2008, WBVPM, NON-INTEGER SIZE FFT: "PERIODIZATION: one period of the input signal is windowed with wR (n) , and repeated several times at the rate defined by T so that the FFT buffer of length M covers in the end several periods. The repetition implies interpolating both the signal samples and the window function. Then the resulting signal sr (n) is windowed by an analysis window function wA (n) , and the spectrum obtained is actually the convolution of such analysis window response WA (f ) by the spectrum of Sr (f ) sampled at harmonic frequencies"

TUNING

I have come up with two techniques for tuning that apply in different ways.

AUTOMATIC TUNING - The idea is that we use a stochastic statistical algorithm that minimizes a cost function by adjusting a set of parameters (one I looked into that seems promising is global-optimization SPSA). The parameters in this cases would be constant used in C. A python script would replace placeholders with the values being picked by the minimization algorithm and then compile and run the C program. The results would then be compared to a reference by another algorithm/program, which would then be summed together to give a cost value. A program for doing I plan to research is called AudioVMAF. I believe it was originally designed to test audio compression, however I hope that it could also be useful here.

MANUAL TUNING - In this method, we insert instrumentation into various intermediate values calculated in the program. Then, for one very small snippet of audio, we use Automatic Tuning to determine ideal values. Then, a programmer tries to write code to make it better match these desired values. Then, if successful, it can be test in general over the whole dataset. If it is not an improvement, then the most negtaively affected audio snippets can be selected and then have a similar process to decrease the change for them while keeping the change for the ones that benefit.

Both of these methods would work best for matching with another vocal synthesizer, since the timings and parameters can match exactly. However, they may also be adaptable to optimizing parameters for real-world (and thus also realistic) voices. It would have to work somewhat in reverse though in that someone would sing first and then a note sequence would have to be made that matches it almost exactly.

OTHER CONSIDERATIONS

There are many more potential tweaks and improvements. I have many dozens accumulated and plenty more to research, test, and implement. One widely applicable variation is using logarithmic based scales.

Comment too long, view post No.178287 to see the full comment.

>Now my version with WBVPM (pitched down by an octave): https://files.catbox.moe/kho97n.wav
For some reason, this link doesn't work. Try https://voca.ro/1mJ5qljrp9hD

uwah... long text... (;´Д`)

speaking as a layman (reading through your post scares me with the amount of stuff i don't know (;´Д`)) this is really cool to see!

keep up the good werk ヽ(´∇`)ノ

ADDENDUM, because I just realized I forgot a bunch of things I meant to put into this post

This is still a simplified model. It does not take into the Excitation plus Resonance model, the Spectral Voice Model. It uses a linear transform and not generated trajectories.

One thing I was thinking about was the part in WBVPM section where they said that one of the disadvantages of WBVPM was not being able to separate harmonic and non-harmonic. I also read that the noise is embedded as fluctuations in the spectrum of each voice pulse and over time, which is what I had presumed because the information has to go somewhere.

I was thinking, what if you took each harmonic as the values and the pulse onsets times as the positions in a spline. Then interpolated at regular intervals. Then applied the fourier transform. Then separate the highest frequencies and the others. Take the others and apply the inverse Fourier transform, and then rebuild a spline from this and interpolate the values back at the onsets. I wonder if this would work.

There would be loss though because of the resampling steps. This could decreased by taking more samples. You could also apply a correction by sampling and sampling it back to calculate the resampling loss itself without the removal of the high frequency modulations, and then add this difference back to the main pulse information after the separation.

badger badger badger

ｷﾀ━━━(ﾟ∀ﾟ)━━━!!

ｷﾀ━━━(ﾟ∀ﾟ)━━━!!

ｷﾀ━━━(ﾟ∀ﾟ)━━━!!

ｷﾀ━━━(ﾟ∀ﾟ)━━━!!

are you dumb motherfuckers satisfied?

yes sir!

>>178235
god bless (´ー`)

Shit!!!!! (;ﾟДﾟ)

i saw the fortune fail thread before this. you're nothing

>>178269
SHHHHHHHHHH SHUT UP

>you're nothing
You're nothing! You're no talent!
I made tons of threads on Heyuri.net, ok?! I got dozens of replies, who the fuck are you?!
WHO THE FUCK ARE YOU?!!

>>178270
ha ha

Your fortune: You have aids

yes...

this made your poop green apparently

no. i ate grocery store sushi and then yogurt and honey. i wanted to cook spam in my air fryer but i wouldn't have been able to fit all of the spam in it at once and i didn't want to cook it in batches.

N

g

ｷﾀ━━━(ﾟ∀ﾟ)━━━!!

not teh cheese!

ｷﾀ━━━(ﾟ∀ﾟ)━━━!!

so horsegame is a gatcha?
i remember when anime was huge a few years ago, never watched it.

>>178222
if you love gambling yes, every part of this game is rng

>>178227
give it a lick

welcome back

>>178207
how could you say that! ヽ(`Д´)ノ

Hana would like this (but won't flash her pussy just because of this).

MUJIINA cute ヽ(´ー｀)ノ

>>178225
this is not taking a stance or declaring a type of relationship so i guess the third reply will have to decide

Iruyeh-tan = comic relief rival to Heyuri-tan whose dastardly plans always fail

Iruyeh-tan != some crying emo girl fag

>>178232
Oh, so she has some relation to this green-haired clover creature?

I assumed she was from some anime I'd never heard of

>>178232
so are you telling me that all iruyeh-tan is to heyuri is a laughing stock? someone you can point and laugh at?

>>178224
iruyeh is an attention seeker

>>178200
brutal!

>someone dressed in black and climbing windows/picking locks at night would have had a pretty easy time looting random houses or assassinating people wouldn't they?
You can still do that in the modern age, just go to some remote rural shithole (preferably in a country where most people don't have gunz)

>>178210
in this day and age even they may have house alarms or something like that

All hail Britannia

The term "out law" comes from when criminals were banished from medieval towns, essentially living outside the jurisdiction of that area.

Bandits would also live outside the kingdom as that was also not easily controllable and what happened outside was not easily punishable as it would be if you did a crime inside a kingdom.

So, being a bandit was quite easy, you just lived in the woods and attacked travelers, and you were outside the law, so it was up to the traveler to defend himself, wasnt the king's job.
A large job for knights was escorting travelers safely because of this.

nom nom nom

Apparently (what I've gotten from my vigorous one google search), rat tastes similar to lamb and rabbit.

b-but doesnt rat eat all sort of shit like pigeons? Thus bad for you?`

what if he suddenly starts moving on the plate

>>178072
Did you do that with writing or pin? Quite impressive if you did it by writing.

>>178076
I did it by pin

>>178072
>Lithuania
>Hungary
Anon...
Also, how did you get anything in Ireland correct? I had to do this one multiple times until I had it all down.

>>178180
Man typing is difficult!

>>178187
Forgot the image