# Tag Info

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Does cubic interpolation (or any other) have any advantages over linear for the specific case of audio? You'd use neither for audio. The reason is simple: The signal models you typically assume for audio signals are very "Fourier-y", to say, they assume that sound is composed of weighted harmonic oscillations, and bandlimited in its nature. Neither linear ...

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When talking about modeling, there are two things that usually get modeled: 1. the guitar amp, and 2. the speaker cabinet. Only the latter is modeled by an impulse response, which means that the cabinet is simply represented by an LTI system and implemented by convolution. This is of course an approximation but it works fairly well. You can find a lot of ...

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Is it possible to reconstruct the original pure signal? No, that is information-theoretical impossible. Also, that signal doesn't exist, probably, to begin with ;) However, you can definitely increase the the SNR simply by averaging; that becomes pretty obvious when you consider the signal of interest to be correlated within your recording, whereas your ...

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If you're an EE student, you will have encountered the term LTI System (or you certainly will soon enough!): A system that, no matter the absolute time, outputs, given the same input, the same output; if you scale the input by a factor, the output is scaled by the same factor. Linear, time-invariant, so to speak. LTI systems can be applied to time-domain ...

6

The fact that you are plotting the FFT of the whole audio clip makes me think that you are looking at the wrong class of solutions. From the waveforms of signal A and B, it is clear that your noise and signal are not stationary. If you want to use a frequency-domain noise reduction technique, this should be done, in your case, on shorter overlapping windows ...

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My own interpretation: Sound: a mechanical wave that propagates through the air or water. Audio: sound in the 20 Hz to 20 kHz range; in other words, sound that is (at least in theory) audible to a large number of humans. Voice: sound produced by the human vocal tract. Speech: intelligible voice (i.e. not grunts or screams) Tone: signal dominated by a ...

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On each individual device, the speaker output can get subtracted from the microphone before it gets sent to other locations. This prevents others from hearing themselves through your microphone. When using two devices in within audible range of each other, the devices cannot subtract the speaker audio from the microphone audio because the information path ...

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The Cocktail Party Problem is a Blind Source Separation (BSS) problem. Given a linear mixture of signals: $$\boldsymbol{y} \left[ n \right] = A \boldsymbol{x} \left[ n \right]$$ We're trying to estimate the signal $\boldsymbol{x} \left[ n \right]$. The model can get even more complex with $A$ being time varying: $$\boldsymbol{y} \left[ n \right] = A \... 5 Audio signals An audio special-purpose analog-to-digital converter (ADC) normally has an internal or external analog low-pass filter and samples the analog filtered signal at a multiple of the target sampling frequency. This high-rate digital signal is then low-pass filtered by a digital decimation filter and decimated to the final sampling frequency. If we ... 5 The consumption time and transmission time is identical: One second of data is still one second of data regardless of sampling time. The greatest common divisor between the two rates is 300, thus to resample this exactly from 44.1KHz to 48KHz you would need to use the ratio 160/147 (and the inverse for the other direction): 147 is factored into 3, 7^... 5 If you calculate the error, 44102/44100 is only about 45 parts per million. That is well within operating tolerance of many crystal oscillators used in consumer equipment to generate audio sampling rates and USB communication clocks. You can be quite pleased it is not even more off. Another problem is that if you simultaneously use different devices for ... 4 The trigonometric functions "do not know" what a Hertz is and they do not care either. The only thing they know is that a full circle is 2 \pi radians. Whether this circle concludes in days, hours, picoseconds or a slice of it represents the angle a force is applied to some lever, is immaterial. 2 \pi \omega expressed in Hertz, denotes a rate. A rate of ... 4 If the noise signal B is highly correlated with signal A (means related through linear filtering) than you can use an adaptive FIR filter to subtract out the noise from signal B and leave just C. If the noises are uncorrelated but just have the same spectrum and/or probability density function, spectral subtraction or Wiener filter (or a combination of both) ... 4 If you have a reference signal you want to find in a different signal then your model matches almost perfectly (Up to the environment the signal to be found is in) to Matched Filter. So basically you need to do cross correlation between the Test Signal and the Reference Signal. Find the point of maximum correlation and create a cropping zone around it ... 4 In simple terms, Image shown here speaks for itself. Before speaking about Fourier Transform black magic lets understand idea behind it. Work of the Mathematician Joseph Fourier demonstrated any arbitrary periodic (this is important) signal can be decomposed into bunch of sine-waves at with their corresponding amplitude and relative phases. Essentially, ... 4 The problem is that the stft function is splitting the signal up into different windows. That means that the signal from time n to n+N_{w}-1 is multiplied by$$ n, n+1, n+2, \ldots, n+N_w-1$$instead of$$ 0, 1, 2, \ldots, N_w-1 $$which is causing the scaling problem. If I apply the group delay calculation from this derivation, I get: where the top ... 4 The usual answer is that that you must convert by integer ratios, therefore 44.1 kHz to 48 kHz requires integer up and down conversions. Since this has been repeated in DSP text books for at least the past 50 years, it's almost always the answer you'll get using a ratio of m/n, where m and n are integers. The typical way uses a windowed sinc function—sinc ... 3 Good question as you can actually undersample and oversample at the same time! See my "DSP Puzzle" question on that specifically here: How do you simultaneously undersample and oversample? To best explain undersampling and oversampling, it is worthwhile understanding the concept of "Nyquist Zones" first. This was explained in detail recently at this post: ... 3 As you mentioned MFCC features are one of the best features to represent audio as it captures both the time and frequency variations in the audio clip.You can get more details about MFCCS features in the below link: http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/ You can import ... 3 Audio quality assessment is one of the most critical pieces of audio coding and enhancing applications. The task requires an accurate and objective (mathematical) modeling of human auditory system including its subjective virtues. However, the task of subjective quality assessment is one of the most complex problems to be attacked on Earth. Currently all ... 3 Sorry MM, I'm agreeing with Havakok on this one: A time domain interpolation solution should do just as well, practically speaking, and be significantly cheaper in terms of computation. (Assuming most frequency content is a ways below Nyquist). I would go with cubic interpolation so you don't have any "corners" at the original sample points, which are of ... 3 As Stanley Pawlukiewicz said: even under ideal circumstance, you can gain 3 dB of SNR per doubling of recordings. I.e., to increase SNR by, say, 15 dB, you'd need to average$$ 2^{\frac{15}{3}} = 2^{5} = 32$$recordings. That alone shows that the whole thing isn't really practical: it just doesn't do much unless you use a crazy-high number of recordings. “... 3 Now is there a general method to analyze and determine the exact function of that effect No, there can't be. Effects can (and will be) arbitrary, non-linear, memory-affected mappings. You will have to make a model of what the effect does and then look for an estimator for the parameters of that model. For example, if you radically restrict yourselft to ... 3 Frequency is mathematically defined as the number of cycles per second. So it is a more strict word mathematically. It is represented numerically by the unit called Hertz. f=1/T, where T represents the one-period length of a waveform. This makes frequency quantifiable. Pitch on the other hand, is a perceptual characteristic of a sound frequency, so it'... 3 Could be a few things You may be borderline clipping. Your sine wave has an amplitude of 1, which is just at the edge of clipping (depending on how its rendered). Try it with an amplitude of 0.5 Your hardware is sloppy. For example, cheap laptop sound cards often cut corners in the anti aliasing filters and or clipping management Your operating system is ... 3 The Explanation from @ScienceGeyser provides a good explanation to the phenomenon. There are two more things to address the question on how is this phenomenon avoided. The feedback read by the microphone is not identical to the audio sent to the speakers. There is the physical response from the speakers, the acoustic of the device and the environment, if ... 3 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 of Acoustic Scenes and Events). The result pages are amazing - you can sort all systems by their performance, classifier being used, features, etc. For example ... 2 Under the assumption that your background noise is statistically independent of the signal you want to recover and that your second microphone picks up virtually nothing from the body noise, you have the following model for your measurements:$$ \begin{eqnarray} M_1(t) &=& L\{N(t)\} + B(t) \\ M_2(t) &=& N(t) \end{eqnarray} Here, $M_1,M_2$ ...

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