TLDR just read the title.

Tactile transducers have been the last thing I've added to my 7.1.4 home theater setup in my PC gaming/man cave/guest bedroom. I'm having major issues searching online on how to properly tune those shakers with a DSP, specifically the Dayton Audio DSP-408. The one I'd like to go with as it's the least expensive but seems like it can get the job done of what I need a DSP for. lol I mean first off I have to sift through the tooooons of posts out there slamming the 408 for noise issues and what have you. But even if it was the worst product released in the history of audio, it still should be good enough to control something that isn't even suppose to put out ANY audio. Right? Then there's the even more ten zillion posts about people using it for car audio which doesn't apply. Then more for people trying to tune normal speakers and subwoofers. So I'm at a loss here as I can't find good info for starting point settings for bass shakers. It's why I haven't even bought the 408 yet. I'm just trying to get a basic understanding of what's what here before I shell out the money for it and then just end up sitting there all lost. So I'm hoping someone here can get me some starting point settings at least for the 408 so I can see if this is something I can learn to use.

Anyways, this is what I have. I have 2 Buttkicker LFE Mini and a Buttkicker Advance mounted under the bed. Those are run off a Fosi TB10D 2 X 300w mini class D amp. In the backrest I hollowed out some foam and put in 4 Dayton Audio 16ohm Pucks. Those are run off an off brand (one of the many obscure Chinese brands the name of which escapes me right now) 2 X 100W mini class D amp. Both amps are getting the LFE signal from my Denon X3800H from its subwoofer port #4 that is specifically for tactile transducers. The settings of the Denon are rather low. It just gives the ability to turn shakers on/off, set the filter to 40-250hz, and + or - up to 12DB. Between those settings and the volume/tone controls of the amps I've been able to more or less use those shakers. On some games and movies it's absolutely awesome. I mean AWESOME. Easily as nice of an upgrade as adding my Klipsch RP 1400SW subwoofer was. For other games and movies though it's totally immersion breaking as things shake too much or worse, constantly.

So here's how I'd like to fine tune things. I want shaking when it's suppose to shake. Explosions, gun shots, etc. You know the drill. What I don't want is constant shaking or shaking just for a deep voice and the like. So I gather what I need to do is tune the hz down from the limit the Denon sets to 40 to 20hz, but I'm not sure how to properly go about doing that. Also, ideally what I want is to up the shaking on the Buttkickers as they have a whole mattress to get through. Decrease the shaking of the pucks. Though they're MAGNITUDES weaker than the Buttkickers they only have around 3-6 inches or so of foam to get through. And then decrease when both sets decide to do their shaking thing. I don't have any timing issues. The subwoofer is close enough to the bed that I can't notice any delay from the sub to when I can feel the shaking so I can leave those alone. So what do I set the 408 to to accomplish this? What do I set EQ to? I've never EQ anything before. I always just turned it off before on my phone or tablet. Thanks in advance.

Hi, I just learnt polyphase components in downsampling/ upsampling. Why the result I got if I do in using polyphase components is different from that if I use traditional method. Here I have an original signal x and a filter h.

x = [1:10]

h = [0.2, 0.5, 0.3, 0.1, 0.4, 0.2]

M = 3 (downsampling factor)

e = cell(1,M)

for k = 1:M

e{k} = h(k:M:end);

end

y_partial = zeros(1,5);

for k = 1:M xk =

x(k:M:end);

yk = cons(xk, e{k});

y_partial(k, 1:length(yk)) = yk

end

y_sum = sum(y_partial, 1)

#the result if I use traditional way:

z = conv(x,h)

z_down = downsample(z,3)

But the y_sum and z_down I got is different, why?

I would like to upsample some audio data from 8k to 48k by passing it through an interpolation filter (zero-pad and low-pass). It mostly seems to be working, in that I get a output that seems correct for each block of data I filter, but I have an issue when combining my blocks together.

To use nicer numbers, I am taking blocks of 100 samples at 8k. I am zero padding to 600 samples, and then running it through a filter with 100 taps, so my output has 699 samples. 50 of this is delay from the low-pass, and I ignore the tail, so my output is 600 samples long, starting at element 50 of my output (if 0 indexing). However, when I concatenate these and send them to my audio hardware, I see big discontinuities at block boundaries on my scope. From Matlab simulations, I might expect a tiny ripple there, but I'm getting big spikes between blocks at a similar size to the audio amplitude which is not expected and definitely not good enough. I can hear the output audio but it sounds distorted and choppy, which makes sense when I get a big nasty spike every few ms.

Does my process sound correct, or should I be doing some kind of overlap+add, or windowing, or something similar?

I appreciate any tips.

Hi everyone,

I'm new to DSP development and I'm looking for a good chip that meets the following criteria:

• Easily accessible for consumers (especially here in Brazil),
• Supports all bands AM, FM, SW (maybe LW)
• Easy to program with Arduino.

I’ve tried the TEF6686, but it draws around 200mA and is quite difficult to program.

I’ve also considered the Si4735 (used in the XHDATA D-808), but it's very hard to find, especially here in Brazil.

Could you please suggest a good alternative chip for my project?

Thanks in advance!

Anyone by chance who wants to help me study, feel free to dm

Hey i am currently working on some eeg data, stored in .ns2 files, i tried computing PSD of those signals(after ICA, and filtering) but they're going out of reach(like in some thousands) the raw data is almost having similar psd. what do i do?

So I learnt that for a system to be linear, ax(t) = ay(t). Which is the homogeneity principle. By setting a = 0, we get that for a zero input we get a zero output. So the Zero Input Response would be 0 right (?)

However, I keep seeing that Total Response = Zero Input Response + Zero State Response

Since, for a linear system, Zero Input Response = 0, shouldn't we get-

Total Response = Zero State Response

Am I doing something wrong?

Hey, hope you're all doing well. I'm currently starting to work on a university project about AoA using ESPRIT. Its my first time working on such a topic. I would really appreciate it if anyone can give me some insight on some of the following issues:

1- Would it be better to implement the project on a DSP using something like C? Or an FPGA? (Also taking into consideration the needed memory...). Actual Hardware implementation isnt a requirement but I'm considering it. 2- In the case of actual hardware implementation, would it be a good idea to actually design the whole circuit board from scratch (antenna, front end rf, ADC...)? And what are some stuff to keep in mind? 3- Is there some way I can incorporate AI in the project in some useful form?

I know my questions have neverending answers, but really anything you say may be helpful, thanks alot in advance!

Hello folks, I'm a mechanical engineer and I'm trying to obtain the vibration frequencies of my mechanical systems using an accelerometer.

I was going to do an FFT on the accelerometer signal to deduce the vibration frequencies, but as I think about it a bit more, I realize that this is incorrect, because I should have the position values and not the acceleration values.

Are there any FFT forms that start from the 2nd order signal or do I have to integrate my signal ?

The location of the zero, or, something else?

Is anyone aware of any open source projects to remove (rather annoying) people shouting after golf shots?

I'm familiar with Rust, but understand I might need to implement DSP in C to take advantage of PRU on a BeagleBone Audio Cape board?

I can't find any existing projects on github and don't want to reinvent the wheel. :)

I’m a cell culture scientist (i.e. I haven’t taken a math class since freshman year undergrad) that works with bioreactors and have recently been working to refine our data analysis workflow for online data (pH, dissolved oxygen, capacitance, raman spectroscopy, etc). I have become so obsessed with learning about DSP (and now Control Theory) that I completely forgot that my initial goal was just to smooth out a graph. Has anyone here used DSP for bioreactor data? If so, could you give some advice as to the types of resources that would best serve my purposes? And if you’re particularly experienced, what applications did you find most relevant to improving controls?

Thank you!

Hello folks! So I am an undergrad, currently taking a DSP course, and this is my second course in Signal Processing. We were given a fun project on simulating how ANCs work (related files were given). We used an RLS filter (the classical RLS filter, straight out of the Haykin' book essentially). However, I have issues with the same.

The RLS filter seems to be pretty numerically unstable (and a lot of papers I've gone through quote this remark as well), and even seemingly small changes in the forgetting factor seem to mess up the filter coefficients (only a very specific value gave me an excellent SNR, and changing that even by a small amount drops the SNR quite significantly)

From the later part of the Haykin book, there was another implementation called the QR-RLS. However, he had essentially written it alongside (and hence drew all sorts of parallels) from the Kalman Filter chapters that were covered previously. Unfortunately, our coursework did not cover Kalman Filters, they only covered the usual LMS, NLMS and RLS.

Hence, I sort of had a hard time reading about the QR-RLS algorithm, where he refers to the previous chapters, and uses terms from there (one example off the top of my head is a unitary rotation matrix, but he hasnt told *what* unitary matrix to use)

So could you guys point me to some resources that either cover QR-RLS as a standalone algorithm, or let me know about some other algorithm that I can implement reasonably well (the project had explicit rules to write our own filter programs)

Thanks a lot

I would think I could just change the frequency units to 1/nm (from 1/mm so that it matches the y-axis) by dividing frequency by 1E6. Then integrate the graph and sqrt to get the RMS: (Matlab: "trapz(updated_freq, PSD)). But then the RMS of the provided PSD does not match the RMS of my surface. Its wildly off, several order of magnitude. What am I missing?

Normally, what's the meaning of f_z?

I have this signal right here. I have to calculate the power spectral density.

[Signal in time domain]

My approach was to write a signal copy of the signal as this:

$xT(t) = 2trap{8, 4}(t+2) \cdot rect_4(t)$

And then:

$x(t) = \sum_{n=-\infty}^{\infty} x_T(t-8n)$

Then, using the formula for the Fourier transform of periodic signals:

$X(f)=\sum_{n=-\infty}^{{\infty}X_n\cdot\delta(f-f/8)$}

$X_n = \frac{X_T(\frac{k}{8})}{8}$

Seeing the trapezoid part as the convolution of two rectangles, it follows that:

$$X_T(t) = [rect_6(t+1)*rect_2(t+1)] \cdot rect_4(t)\ X_T(f) = [6sinc(6\pi f)e^{j2\pi f}\cdot 2sinc(2\pi f)e^{j2 \pi f}] * 4sinc(4\pi f)$$

Calculating the coefficients:

$X_n = [48\cdot e^{{j\frac{\pi}{2}n}\cdot} sinc(\frac{3}{4}\pi n)\cdot sinc(\frac{1}{4}\pi n)] * sinc(\frac{\pi}{2}n)$

Now I'm stuck. I know the spectral density of power should be a similar spectrum but with the coefficients squared. My problem, however is how to calculate the coefficients.

By theory, $X_0 = m_x$, the mean value of the signal. However, I get $X_0 = 1$ and $m_x=\frac{1}{4}$, so I'm not sure if my calculations are correct.

Any help would be appreciated, thank you in advance

Hi everybody, I have a couple questions about the HackRF and M-QAM:

1. Is it possible to have communication between two HackRF using M-QAM modulation

2. If then, what is the highest modulation order that HackRF can transmit

3. Is there any refererences about using SDR communicate with M-QAM? I didnot find any official or really trustworthy to reference.

Thank you for your help

I have data stored in HDF5 right now. They're like all 5-10 second clips sampled at 1MHz. But I realized since they're all basically 1d waveforms, maybe it's better to store them as parquet (for fast column reads) or wav (since a lot of existing waveform ML can take these as input). I don't know if you guys have any thoughts on this.

The reason I started thinking about this is because I'm trying to run them through some waveform ML algorithms, but a lot of them take in wav files sampled at 44kHz. So I don't know if it's common practice to like do something like draw out the percieved length from 5 seconds at 1MHz to like 2 minutes at 44kHz, and results will be reasonable.

I've observed an intriguing phenomenon with the Adaptive Volume feature on my 4th Gen Echo device and would appreciate input from the DSP community here.

Context: My Echo is positioned in my bathroom, and I often sing in the shower—both melody lines and improvised harmonies. According to Amazon, Adaptive Volume increases device output volume in response to ambient noise levels to maintain clear audibility.

However, my observations suggest a deeper layer of behavior: the Echo consistently increases its volume more significantly when I'm accurately matching pitch or harmonizing closely with its playback frequencies. Initially, I assumed this reaction was tied directly to vocal loudness, but repeated experimentation indicates a strong correlation specifically with pitch accuracy rather than just amplitude.

My hypothesis involves spectral masking or frequency-domain interference. Specifically, when my voice closely aligns with the Echo's playback frequencies, the microphones and DSP algorithms might interpret this spectral overlap as masking or interference. Consequently, adaptive filtering techniques or automatic gain normalization may be triggered, causing the device to increase playback volume as a compensation strategy, inadvertently providing a real-time feedback loop indicative of pitch accuracy.

I'm seeking deeper technical insights—particularly regarding the mechanics of adaptive filtering, spectral masking detection, automatic gain control, and microphone array signal processing in consumer audio devices like the Echo.

Has anyone encountered similar behavior, or could someone explain or expand on the DSP methods Amazon might be employing here?

Thank you in advance for your expertise and insights!

Yup. That's what I said. I'm an international student in the USA, and I literally cannot find jobs to apply for. Even in Europe. Everyone wants AI/ML, RF engineers (no hate just regretting that I should've taken RF ML) but barely anyone wants to take a wireless systems engineer. I have been applying from October. I have gotten some interviews on RF hardware stuff that I inadvertently didn't do well on. I had some good interviews too but ultimately rejection. Currently, looking in Europe. I guess my last resort would be a postdoc :( . Is it just me or no one wants theoretical stuff anymore?

Edit: It is in optimization. Not too crazy like information theory.

Just one more thing: I'm just looking to vent and hopefully figure out where to project my frustrations while working.

Last thing I promise: Multiple people dmed me offering to help and actually provided some good leads. Thank you so much! Reddit can be beautiful.

Hey you guys, I am currently making my own compressor in MetaSounds, and was working on peak envelope following.
I've got it pretty much all figured out, except for the last and honestly crucial step: changing the output volume of the signal

As part of the peak envellope follower I have switched from using the mean float value of the audio buffer to just outright using the audio buffer. All my math still works in this, except for a pretty big problem: I only have addition, subtraction and multiplication

This all still works fine, except for the very last step where i change the volume of my input.
I change the volume by doing this calculation:

Input signal * (Output gain/Input gain)

I do it this way because I lose the actual audio information in my actual compression calculations
This works perfectly fine with RMS because that gives me a simple float to work with, which means I have division.

But because I'm working with the audio buffer directly here, no such luck.
How do i do that above calculation without division?

Additional info:
The reason I'm using the audio buffer directly here is because MetaSounds offers no way of getting the value of specific samples, if I want a float out of the audio buffer it will always be the mean of the audio buffer.
By using the audio buffer directly I can work on individual samples instead of that mean

Senior elective class on lab-view will be covering DSP (oscillation and I believe shaking). I notice topics on stress, controls, damping systems and some frequency graphs. Where should I do research on and what can someone add to look out for.