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Let me edit your statement to totally change its meaning, but which question I think you should ask. Is there ... a ... "proper" manner? If you had asked this, and had meant "is there any universally accepted yardstick by which one processes audio?" Well, no. You do what sounds good (if it's for artistic purposes), or what's most ...


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Fully agreeing with Hilmar, to add to Is there any other way this could be done in a more "proper" manner? Well, you can try to make sure that the spectrum of your signal stays the same, without the discontinuity. The way I'd approach that mathematically: Take a low-pass filter. In this case, preferably a FIR, for you'll need the impulse ...


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But is this good practice? Yes, that's a perfectly reasonable approach. Will this will work for all types of signals I might have ? There are a LOT of signals out there and there is always an outlier. In your case, I think the most "vulnerable" would be low frequency sine waves. A 40 Hz sine wave has a period of 25 ms and looping one at an ...


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Zero-padding the beginning and/or end of your data is a good approach that does not induce irrelevant information to your original data. One effective choice is going into the frequency domain. You can apply FFT to your time series and consider first m FFT coefficients as your new data. It is a fair assumption because higher frequencies are usually ...


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The mel spectrogram additionally includes a step of projecting (power of) STFT bins onto Mel-frequency bins via a Mel filterbank; I don't have access to path so I made demo on exponential chirp: You can visualize the kind of projection taking place by plotting the mel basis: Note in general the two won't look alike unless filters.mel are carefully selected ...


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Q1: Sample values in .wav file represent the waveform amplitude? Yes. Q2: What is silence? That depends on how you define it. The signal that you generated actually has a lot of energy but it's all at 0 Hz. Your DAC cannot reproduce 0 Hz, your speaker cannot reproduce 0 Hz, air cannot transmit 0 Hz, and your ear can't hear 0 Hz. There is lots of energy ...


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What is missing from your calculations is that it requires a multiplier of two because CD quality audio is two channels for stereo. 2 * 44.1 kHz * 16 = 1411.2 kbps The answer to your second question is twofold. First of all, human hearing range is typically said to go up to 20 kHz, not to 44.1 kHz. However to sample data up to 20kHz, it needs to be sampled ...


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what is the purpose of CAR Equalization in audio Car have a lot of individual speakers in many non-optimal locations. In order to get a good listening experience equalization is used to adjust frequency response and delays for each individual speaker. why before CAR Equalization ,gain adjustments are necessary ? Sorry, I don't understand the question. ...


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What is the purpose of Balance / Fader in a digital Audio amplifier? In a stereo amplifier the balance controls allows to set different gains for the left and right channel. That would be useful if the listener is not located right on on the center line between the speakers or if the acoustic loading the two speakers is very different. The balance control ...


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This effect is usually undesirable and is prevented by (acoustic) echo cancellation algorithms. These algorithms estimate the distortion of the incoming signal on its way to the microphone (via the loudspeaker and the room acoustics). That distortion is modeled by a filter and the output of that filter is subtracted from the microphone signal, such that the ...


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Theoretically, the error between the original and the reconstructed signal goes up. Practically, for actual audio, if you're lowpass filtering too much, the audio starts to sound muffled or muddy -- like you're trying to listen to the event through a pillow. Again in theory, if you've got a bandlimited signal mixed with noise, the best processing for that ...


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Simple Fourier Transform propery for differentiation is $$\mathcal{F}\{\frac{\partial}{\partial t}x(t)\}=j\omega \cdot\mathcal{F}\{x(t)\}$$ Differentiation in time corresponds to multiplication with $j\omega$ in frequency. Hence the +6dB/octave slope. The phase shift is a constant 90 degrees for all frequencies.


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i dunno, something like this product might help. you need to find yourself a good Faraday Cage or construct one. maybe you could build a small cubical room out of wood and cardboard that is 2 meters on a side. line the entire interior with good aluminum foil, make sure the adjacent pieces are well connected with multiple fold contact. connect that to a ...


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It is very hard to detect a device that is passively receiving a signal, and even more so when it is designed to operate undetected. Some random comments and ideas: In the UK one has to pay taxes on TV ownership. I read somewhere that they employed a technique to detect unlicensed TVs, which are otherwise passive receivers. I don't know how the technique ...


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It is very difficult to answer this question directly because there is not much information available. It's almost more of a reverse engineering question, because you are trying to discover how a device you do not have full knowledge of works. Nevertheless, I can suggest some things that it might help you think about: When you say the device does not ...


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Assuming that the system response is smooth, without narrow peaks or narrow valeys. And that the spectrum of the input signal does not vary so much from one chunk to another. I think this approach is better than doing a single FFT, as you know the FFT of a random signal will have lots of randomness. Averaging may also remove effects due to quantization ...


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I had a look at using both CamillaDSP and doing filtering directly in Python, but the learning curve for both seemed steep. What I found instead is a LADSPA filter called ACDf, which provides a multiband parametric equalizer that can be used directly with alsa. The steps to getting this to work are quite simple: Download the ACDf source code make it to ...


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There are a few different ways: Use a fractional multiply or data types. Unfortunately that's not a standard "C" or "C++" instruction but most embedded processors do support it and have some sort of non-standard way to call it. Use a "long int" 64-bit integer data type, do a double precision multiply. Convert everything to ...


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I wouldn't recommend amplifying the low signal because that will increase noise significantly. As was pointed out above, it's better to downmix to mono. Open your recording in a DAW (e.g. Audacity), delete the quiet channel, then copy the loud signal into the deleted channel. Voila!


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Here's a simplified overview: Start with L and R signals. Run an fft on each to extract Mag and Phase at every frequency bin. Compare the Mag and Phase values of the L and R signals. If they're "the same" within a predefined range (for example within +/- 3dB and +/- 30 degrees), assign this as "center" content. For content that is ...


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Audio up-mixers are fairly complicated and the "better" ones are either protected IP or trade secrets. To extract a center channel you would look at the correlation between the left and right input channels and dynamically steer the correlated content towards the center and the uncorrelated towards left and right. The devil is in the details though:...


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The "cool" stuff that you hear has almost nothing to do with the noise itself, but it's all in the post-processing: They use aggressive time variant low-pass and band-pass filtering, manual gain modulation and reverb/echo effects. Any old pink noise will do just fine for this purpose. "White noise" is an application specific misnomer. ...


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White noise has an unambiguous definition: it's uncorrelated noise. So, anything called white noise was most definitely not high-pass filtered or spectrally shaped specifically: it's got a flat spectrum, by definition. That should sound relatively boring to the human listener: that's inherent to the spectrum. You've got freedom in determining the amplitude ...


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Maybe, when $D$ changes, you can crossfade from the audio at the old $D$ to audio at the new $D$. If you ran a pitch detector alongside of the delay (and stored the detected period length in a corresponding array) you could crossfade one period at a time until you got to within a period of your new $D$. Then slide very slowly to the exact $D$.


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One way to do this is to have a simple one pole smoothing filter. The one-pole lowpass filter is often used to smooth noisy signals to seek slow-moving trends in them. The one pole filter is defined as $$\begin{aligned} y(n) = x(n) + a_1 y(n-1)\\ H(z) = \frac{Y(z)}{X(z)} = \frac{1}{1-a_1z^{-1}} \end{aligned}$$ The pole is located at $z = a_1$. In such ...


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You can simply implement this as an IIR filter directly in Python using https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.sosfilt.html Each Room EQ wizard section can be turned into a second order filter section using RBJs Audio EQ Cookbook (See for example https://webaudio.github.io/Audio-EQ-Cookbook/audio-eq-cookbook.html or https://www....


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ASIO4ALL is a hardware independent low latency ASIO driver for WDM audio devices. As being a (wrapper) driver it does not support programming other but change of settings (device/channels selection, bit depth, sample rate, I/O buffers (latency)) through control software. Regarding DSP programming tools, could you mean Creative/SoundBlaster specific kxproject ...


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For spectrogram similarity, I suggest using a metric from image processing known as SSIM - Structural Similarity Index Metric. It is a quantity between $[0,1]$ which denotes the similarity between two images. The paper describing SSIM can be found here, and the MATLAB function ssimval will give you the metric.


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Let there be two microphones, $x_1(t), x_2(t)$ capturing a source, $s(t)$, with some additive noise $n_{1,2}(t)$ that is uncorrelated with the source and with each other (i.e., its effect can be ignored while calculating the cross-correlation). Let $\tau_0$ (in seconds) be the time difference of arrival between the two microphones. The time domain equations ...


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unbuffer in Python; helpful post. Nice visuals in MATLAB docs (bottom).


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The LFO rate is the speed of the delay modulation. One full cycle is going from "down" to up" back to "down" again. It's in Hz, i.e. "per second". Let's says the music is 120 bpm and your LFO rate is 0.5Hz. 120 pbm is 2 beats per second or 4 beats per flanger cycle. Beats Per Cycle = Music_speed/60/flanger_rate


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