jojek
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Audio classification without deep learning
3 votes

From what you've mentioned it looks like the task is for environmental sound event detection. I think that the best starting point for you is to check the DCASE challenge (Detection and Classification ...

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How can an audio segment report an integrated loudness (LUFS) greater than 0
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1 votes

I did download this annoying video and analyzed the part with a vuvuzela. You should take a note that: This part is clipped The audio is stereo And that pretty much solves the mystery. If you ...

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How do I get the volume levels from an audio file?
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3 votes

I can give you a quick and hacky solution with sox that can be easily installed on any Linux distribution. sox in.wav -n trim 0 0.1 stats : newfile : restart 2>&1 | grep 'RMS lev dB' | awk '{ ...

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Working with a sound's magnitude instead of amplitude
0 votes

I am not sure what do you mean by magnitude, but you should be getting a waveform that's perfectly fine for further analysis you wish to do. Just keep in mind that you don't know the frequency ...

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Convolution : Anechoic Speech signal and Impulse response
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3 votes

I have no access to your audio files so I've downloaded: IR from here (mono/r1_omni.wav) - it's a really long one Anechoic recording from here (operatic-voice/mono/singing.wav) Resampled voice ...

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filterbank: understand the different responses at the center frequency of each filter
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2 votes

Normalizing the filterbanks by their widths is optional and totally up to you (similarly to the warping scale Mel/Bark). Depending on your application, you can start without normalization and see what ...

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How to normalize frequency response charts
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2 votes

First of all, I wouldn't worry too much about the speaker response since it is relatively flat and the microphone has a much bigger roll-off. Since you've captured the frequency response using sweep,...

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FFT on a random signal
2 votes

First of all you should take the magnitude of the FFT (use abs function) - what you've plotted is just a real part of FFT. Secondly, depending on what you want to achieve, I would suggest to detrend ...

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Converting mel spectrogram to spectrogram
3 votes

Nowadays the easiest thing would be to use librosa for this task. It has the mel_to_stft function which does exactly what you want. As others have mentioned, this reconstruction is lossy and only ...

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Two voice pronunciation comparison similarity MFCC + DTW
4 votes

I will answer your questions in reverse order. 4: DTW (Dynamic Time Warping) is not a library but an algorithm. It allows aligning two sequences by warping them in time. You can use it for pretty ...

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Baby Cry recognition robust against the distance
1 votes

This question is very broad and it is not possible to give a very good answer without even knowing the internals of your system (the type of the network and features make a big difference). However, I ...

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MFCC window size at different sampling rates
1 votes

I will answer your question from my experience. For most of the time, I've been using window length which is a power of 2, trying a few overlap percentages, training the system and picking the one ...

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Explanation of OASPL and SEL
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1 votes

Yes, you can calculate it by summing all of the 3rd-octave band pressures. Keep in mind, that if the units are pressure then simple sum and conversion to the decibel scale is enough. However, if you ...

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DSP Concepts Visually Explained
6 votes

Personally, I very much like the interactive visualisations of filters that connect various bits together. There is a great website called MicroModeller DSP (I am not affiliated with it). You can ...

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Is it accurate to say in digital audio that, when a fader is down, then its value is "-$\infty$"?
9 votes

If by "its" you mean the value of the fader, then yes - it's absolutely correct. Fader defines the attenuation of the signal with respect to the reference level. The units are in logarithmic scale. ...

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Apply Low pass Butterworth filter in Python
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10 votes

You should not be using the analog filter - use a digital filter instead. You want the filter to be defined in Z-domain, not S-domain. Also, you should define the time vector with known sampling ...

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Create a third octave spectrum from a time signal
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1 votes

Hate to answer this question, but it is a plotting issue, not the actual design problem. Basically, you don't have enough points at low frequencies cause they are spaced linearly and you are plotting ...

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The Technology Behind Animoji by Apple
3 votes

It's more like a soft answer (I am happy to update it later), but Alex Acero explained the technology behind aniomoji on this years ICASSP 2018. Here is the link. Basically, they are using so-called ...

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STFT amplitude normalization, librosa library
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5 votes

I did answer a similar question a few years back, but can't find it. Basically, you are losing the energy because of the windowing. It's true to say that you should multiply the spectra by 2 in order ...

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How to implement a filter associated to a specific wavelet
3 votes

You've mentioned Butterworth filters for doing the wavelet analysis using bior6.8. If you want to perform the Discrete Wavelet Transform using some specific wavelet, then you must use its Perfect ...

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Are there any open source libraries for Adaptive noise cancellation?
3 votes

If you are working on chat application (presumably web), then I suggest to take a look at WebRTC. It offers a noise suppressor that works ok for speech. Another option would be to use part of the ...

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Issue with the time vector returned by $\tt signal.spectrogram$ function
4 votes

The default parameters of signal.spectrogram are: nperseg = 256 noverlap = nperseg/8 = 32 This means that: The length of analysis window is $256$ samples ($256/250 = 1.024$ second) The overlap ...

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Remove a known wav file from recorded file
2 votes

Description of Source Separation Algorithm The approach that I would take is based on a Semi-supervised Non-negative Matrix Factorization. This would work assuming that: The audio file that you are ...

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Calculate algorithm's MIPS from cycles?
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0 votes

The Naive way is to do 6 846 310 893 / 45 = 152 140 242 ~ 152 MIPS. This makes an assumption that each instruction takes exactly one cycle on your processor. Another approach would be to use tool ...

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Measuring noise floor in an impulse response
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2 votes

I can suggest you take a look at the supporting information for Dirac software, where they describe the process of INR (IR to Noise Ratio) calculation. The simplest approach is to estimate the noise ...

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FFT of two sets of samples vs FFT of sum of samples
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2 votes

Fourier Transform is linear, hence if $\mathcal F[x(t)]=X(f)$ and $a$, $b$ are complex numbers, then: $$\mathcal{F}[ax(t)+by(t)] = a X(f) + bY(f) $$ So in your case, simply sum the results together.

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What will be the code for this following signal in MATLAB?
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4 votes

Well, so far using your code, you are getting the following: clc; clear all; close all; t = 0:.1:10; f=.5; y=.2*sin(2*pi*f*t); plot(t,abs(y)); axis([0 10 -2 2]); grid on; So now, the only missing ...

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periodicity coefficient
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4 votes

I suggest using Spectral Flatness, aka Wiener Entropy. It is defined as a ratio of geometric and arithmetic mean of the magnitude spectra $X(k)$: $$\Xi=\dfrac{\sqrt[K]{\prod_{k=0}^{K} X(k)}}{\frac{1}{...

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Calculating the inverse filter for the (exponential) sine sweep Method
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5 votes

Assuming that your Exponential Sweep Sine was generated using the formula: $$x(t)=\sin\left(\frac{2\pi f_1 T}{R}\left(e^{\frac{t R}{T}} -1\right) \right)$$ where: $f_1, f_2$ - Initial and final ...

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Forward Backward filter Scipy - $\tt signal.filtfilt$ changes the amplitude when $\tt signal.butter$ is used with $\tt btype='high'$
1 votes

It looks like your signal has a DC offset, i.e. its average is non-zero (approximately $1$ in this case). This component is captured by the 0'th frequency bin. So if a high-pass filter is applied to ...

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