ReasonHead

I need to do some interpolation and would like to keep it efficient. What I missed on my research on CIC filters is that they do not work in floating-point because of the pole on the unit circle being unstable when any kind of rounding error is introduced. Is there some kind of "floating point safe" version of the CIC filter? Or do I have to resort to alternatives, like windowed sinc or moving average?

I have a recording of a classical piece for cello, recorded in a fairly dry room with a close A/B pair. Not ideal, but the best I had access to during quarantine. The client requested to add reverb to add so that it sounds more like a real concert hall. I've tried to find impulse responses of reputable spaces but couldn't find any so I went back to algorithmic reverbs. What settings should I look out for when trying to design a digital reverb that sounds as close to a real concert hall as possible? I've already cut quite a bit of low frequencies on the dry recording to make it sound farther away but the reverb sounds artificial. Granted, the recording is not 100% dry so the faint sound of a

Sorry for the weird title and maybe wrong sub but I don't know where to look. I have a printer at home but (of course) only have the basic white paper to print on. In my experience, commercially available music (especially classical) uses this kind of yellowish paper that often has a lot more texture too. Does anyone know what this is called and how I can get my hands on some? I would love to print my own music and that gorgeous kind of thing.

I was reading through this article that was recently posted here. Near the end, the author mentions compensation FIR filters to "sharpen the CIC filter's frequency roll-off and flatten the CIC filter's passband gain". He provided an example in figure 13 with the coefficients in the text below. How do I find the coefficients for such a filter? In the section after that example, he only provides coefficients for a 3-tap FIR filter that is just useful to compensate for the DC gain but not to sharpen the roll-off or flatten the passband (at least as far as I understood). I'm not very proficient in designing such FIR filters as I assume I need to

I only ever see polyphase filters mentioned for interpolation/decimation filters. Are they always faster than doing FFT convolution? Or are the number of taps too few to justify going to the frequency domain?

So, I have more trouble with this paper...

The solution for anti-aliased waveshaping using a rectangular anti-alias filter is (9). However, if I use this formula to process some test audio, it does not work at all. Using (21) for processing, for example, leads to jumbled audio and the same happens if I use any other waveshaping function with their corresponding antiderivative. Though it could be that my implementation is not right, plugging (21) into a graphing calculator with x(t) = t shows the waveshape curve being shifted 0.5 to the right. I don't know why this happens but interestingly, [a paper that builds on this idea o

Talking about this one.

I first tried to implement just the solution but when it didn't work I decided to dig deeper and fully understand how the new method for waveshaping is derived.

I understand it up until eq. 7 and have a few questions now:

1. Right before eq. 7 the author says that u = τ. But u goes from 0 to 1 while τ goes from 1 to 0. I am not 100% sure about this but I guess it is still the same because if we think of integration as a sum, we can just as well start from the upper bound and sum the rectangles from there, decreasing du until it reaches the lower bound. Is that correct?
2. In eq. 7 the author just wri

I.e. could you just replace e^-jyx with any other periodic function and get some continuous spectrum? Or is there something about sinusoids that makes them the only functions that work? Or do the periodic functions you use need to have special properties (like no discontinuities in any of the derivatives)?

I am asking because people always say that "you can construct anything out of sine waves" and that they are like the basic building blocks. I am wondering whether that is actually true or if sines just happen to be a convenient way of describing certain behaviour and psychoacoustic phenomena so that's what's always used. IIRC from a 3blue1brown video even something simple like a pendulum

He's a rapper and doesn't know anything about AE. I've asked for references but the ones he provided have very soft/smooth vocals while his voice is very aggressive.

• If I put on the same amount of autotune as the references it's not as audible because of the distorted voice he has.

• The type and amount of reverb the references use also doesn't sound as good on his voice.

• Pitching the vocal track up an octave or playing around with the formant doesn't make it better.

• Chorus/phasers are also not a good choice.

• Guitar amp in parallel works okay-ish.

• Doubles don't fit the vibe of the track.

That's about all I know to do in terms of "cool fx". What else could I try? I need somet

Do y'all use something like this? It feels like I've looked everywhere but large android tablets (>13") are nowhere to be found and dedicated solutions are rare and very pricey. I've come to the point where my library of sheet music is too large to have all in printed form and I'd like to go digital.

Recently, I've noticed the "oh yeah" in Ariana Grande's "no tears left to cry" fade out aggressively and I cannot help but find it weird every time I hear it now. Feels like there is a lot of reverb on it that is cut off as well with the fade out.

Also, I am pretty certain Marshmello messed up the volume automation, making it abrupt, instead of smooth in "Wolves" right before the chorus starts. You can hear the reverb suddenly get louder.

What are your examples?

I.e. f(t) = a_1 * sin(t + p_1) + a_2 * sin(2t + p_2) + a_3 * sin(3t + p_3) ... + a_n * sin(n*t + p_n)

Given a_1 ... a_n, can I find phase offsets p_1 ... p_n such that I get a minimum peak value?

I was reading an article on ribbon mics and foil thickness and the author said that thinner foils don't give the ribbon mic more high frequencies but "better quality high end" and "better transient response". Isn't more accurate transient response the same as less high frequency roll-off? Or does transient response differ a lot through narrow peaks/valleys in the high end of the frequency response that are being smoothed in normal frequency response plots which thinner foils improve on?

I've heard this a few times and am curious if it is just a result of mixes being comparatively bass heavy due to the fletcher munson curves and physical laws or if there were some other, physical properties that would change the "energy" (whatever that means in this context) based on frequency. If I'm not mistaken, higher frequencies actually have more energy, physically speaking so I'm kinda confused now.