r/DSP

Hi everyone,

I'm working on a puzzle and found a CSV file that appears to contain data of an audio output FFT. I don't have a background in signal processing, so I'm looking for some help i ran a script but only found an chipping sounds with a "Evil Laugh".

I also see audio in spectogram but there is also nothing.

It has columns for Magnitude and Phase. about 1047170 rows.

File name output_fft.csv

link to download

CSV file

Thanks, for any help!

I remember compressed sensing was a very hot field in the 2010s. I was in school back then and briefly learned it in an advanced linear algebra course. The theory seems beautiful but the only real world application so far I could find is MRI. Nor are there many new literatures about CS on google scholar in 2025 and 26. Is it still too new or dying already? Now I’m back in school pursuing a masters degree. Is CS worth learning over the AIML stuffs (I’m choosing between CS and reinforcement learning)?

I got accepted into master's program for computer science but got rejections for both electrical engineering and communication engineering. How much of a disadvantage is that when applying for roles in radar/automotive/audio processing?
Obviously I have a lot of freedom with regards to classes I pick and will be able to heavily emphasize EE subjects if I wish to, however most of these roles seem to primarily look for people with EE foundations.
On the other hand, a lot of roles in signal processing seem be to heavily leaning into machine learning which would be an advantage if anything, since cs curriculum tends to be more ml-heavy.

Would be very interested to hear from people working or hiring for positions in areas related to DSP.

Thanks in advance

Hey everyone,

I’ve designed a reconfigurable uniform polyphase channelizer optimized for FPGAs, targeting two major pain points in conventional implementations: limited configurability and excessive hardware resource overhead.

Compared with classic polyphase channelizer architectures, this design supports one-time hardware instantiation with runtime reconfiguration, fully pipelined dataflow, and single-cycle throughput.

Here are the core technical capabilities:

1. The hardware is sized for a maximum channel count C and subfilter tap length K. After deployment, it can dynamically work with any channel count c that is a power-of-two divisor of C.
2. Under any selected channel count c, the design supports arbitrary decimation factors ranging from 1 up to c.
3. It accepts arbitrary filter coefficient sets with total tap lengths less than \(c \times K\).

In terms of FPGA resource consumption, the overhead is nearly identical to a standalone decimation filter of length K paired with a serial C-point IFFT block.

I’ve conducted a thorough literature and patent search, and I cannot find any existing published work or granted patents that deliver this full set of flexible reconfiguration capabilities.

State-of-the-art polyphase channelizer implementations only optimize resource usage or throughput for fixed, narrow application scenarios. Most existing designs are constrained by serpentine shift registers and circular output shifting logic, which rules out flexible runtime reconfiguration.

Many fixed-function channelizers achieve higher raw throughput, but that performance comes at the cost of rigid hardware partitioning and locked parameter modes. I believe further speed optimizations are still possible within my flexible architecture without sacrificing its reconfigurability.

I’m reaching out to ask for industry/research perspective: does this reconfigurable polyphase channelizer hardware architecture carry meaningful commercial or IP licensing value?

Thanks a lot for any insights!

Sto considerando di investire per collaborare con Amazon come dsp, qualche consiglio? Ne vale la pena

Title should be self-explanatory and I'm mainly talking about music here.

An obvious/naive example would be something like a spectrogram.
Are there some other ways to process and compare audio files in a way that actually relates to the way humans perceive them (perhaps tempo, genre, mood, etc.)?

could anyone give me a high level answer to this question and where i can find low-level resources?
thank you

Same as title

Could you guys suggest top 2 books that you studied to enhance y'all's knowledge in image processing ??

Hello everyone, I will shortly be beginning my 3rd year of electrical engineering. I have taken the signal and systems course but DSP will be one of the subjects in the upcoming sems.

Though i have keen interest in aerospace industries and wanted do some projects. I was following the book by INGLEY AND PROAKIS to learn DSP along with MATLAB. I have completed till the basic design techniques of the FIR, IIR filters.

The problem i am stuck on was that i can't think of any relevant DSP projects as per my interests. Anything related to avionics is mostly radar stuff. I tried skimming through that stuff but i realised i had to take some radar related courses to get hold of it and make something.

And most other ideas i find are related to image processing, audio processing etc. A week ago i was tired of this shit and finally decided to make something.

I began the project of writing a jpeg decoder and today as i am close to completing it, i realised i learnt nothing of actually the things i wanted to learn😭😭. Man i don't know what to do.

I have previously read and introductory book on kalman filters by Alex becker and made an attitude estimation kalman filter with help of quaternions. Though it isn't worth direct application to a vechile i still consider it a good unique relevant project.

I want to make another such project like that but i can't get any good ideas. If you have any suggestions please help me in comments.

I’m learning MATLAB mainly for DSP, but I feel stuck. Studying MATLAB syntax by itself feels dry, and textbook exercises often feel like I’m just rewriting formulas in code. I can implement things like convolution, FFT, filters, and signal plots, but I don’t feel like I’m gaining a deeper understanding of MATLAB or DSP.Can anyone gives me some suggestions,thanks

Please also elaborate on the challenges...

Hey there!

I’m a recent graduate in biomedical engineering and I just started working on biosignal processing for a wearables startup. I really love the field, but as it’s an early stage start-up, I don’t have anybody senior to learn from (it’s just me and AI right now :D)

I know the theoretical basics of signal processing (transforms, simple filters etc), but I’d like to understand what i should study next. Also, i would like to learn more about best practices in terms of actually implementing DSP in production-level applications. Can you recommend any books, channels or other resources? Thanks :)

So, for starters, I have already read through these posts

Krasjet

RBJ

My mental model before reading these posts only considered intra fourier theory logic. What justifies the transform is finding the characters mapping of the source domain group to the destination domain group. These, under the right Hilbert space, form an orthogonal, complete spanning basis in all four fourier theories (easiest to see if we treat raw functions as infinite length vectors; in the discrete case, even easier) so you have the nice "fourier analysis is the decomposition of a function into constitutent frequencies" part. I was always better with abstract algebra than continuous math (trained in CS primarily lol), so this clicked with me (the character mapping part) before the linear algebra part, funnily enough.

Initially I was satisfied with that. But then I got confused about how we can justifiably go from 1 theory to the other if the domains. importantly, one question has been bugging me: "What does it mean to evaluate a fourier transform at a non DFT bin?"

I dug into textbooks and found this gem. Here, alongside RBJ's stackexchange answer, there is a direct conection there - a zero-support array in DTFT land is exactly the same as the DFT, mathematically speaking.

But how? I mean, yes, the algebra works. But another convincing way to arrive at it is precisely krasjet's possion-summation translations between theories, which repeatedly hammer home the relationship between periodization = sampling in the opposite domain.

To me, it seems like the answer to:

> "What does it mean to evaluate a fourier transform at a non DFT bin?"

is a bit shaky. Do you evaluate it in DTFT land, using the zero-support equivalence? But is that justified? Didn't we say the DFT was periodic (which I agree, the math checks out)? But the DTFT with zero-support looks aperiodic?

There's also the issue from the other domain: In the R/2pi -> Z domain, "evaluating at a non DFT bin" would correspond to evaluating at a rotating basis that doesn't actually cancel out to zero after you rotate for 2pi time (e.g. a non 2pi periodic frequency). So from that side, I want to say it's undefined. Is it just a nonsensical question? Maybe you project it, it returns an actual complex number - but it's nonsense? It doesn't typecheck?

At this point, I've worked with some basic RF DSP algorithms at this point, and almost always you avoid this issue by construction. I've seen these two approaches for frequency shenanigans:

• If you need finer grained DFT bins, you send your symbol over a longer duration.
• There's other tricks like, you raw modulate a sin wave for 1 period at a certain frequency. Then, you effectively "project" that sin wave against itself via a matched filter (I think it turns out the matched filter is phase-invariant which is a neat trick too). This is how I've seen FSK implemented.

I mean, they work, it just feels... not rigorous, I'd say. I mean, all the math checks out internally, but again - how robust is it to, "We'll extend by 1 sample" or "We'll compute it at an off-frequency" or something.

In some sense, DSP is easier to think about for me, if we just think about it as pre-agreeing to byte buffers of a certain length and having convenient invertible functions.

Which is obviously a wrong long-term way to look at the field, but I'm really not comfortable working with these objects outside of pre-set paradigms. It seems to me that there should be some notion of 'frequency' that truly unifies across theories (krasjet seriously has the best resource out there), but right now it seems like the notion of frequency is way too coupled to the representational access you use to get into the domain, which is weird because we talk about frequency being an intrinsic property of waveforms. (I mean, fourier analysis works on any bounded square-integrable complex-valued function. It clearly is an intrinsic property of a function. But extracting how much of that information translates between domains, when suddenly periodicty assumptions show up, is weird).

And not just in a "Oh more samples = more frequency" kind of way. That's exactly the issue I want to avoid. Not sure how to formalize this question.

Hopefully this context dump is answerable. If not, then I just got a good vent out :)