# Tag Info

28

Does the Nyquist frequency of the Cochlear nerve impose the fundamental limit on human hearing? No. A quick run-through the human auditory system: The outer ear (pinnae, ear canal), spatially "encodes" the sound direction of incidence and funnel the sound pressure towards the ear drum, which converts sound into physical motions, i.e. mechanical ...

17

The closest example I can think of is the beginning of Suzanne Vega's "Tom's Diner" which has been used for the mpeg-1 layer 3 development, and is still occasionally used to demo audio codecs.

17

No. Why do you think it would? First of all, the human brain works very different then any human constructed computer (to date); so the assumption that it runs mathematical "algorithms" is somewhat dicey.. Here is roughly how it works: Sound wiggles the air drum That vibration is transferred by the ossicles to the cochlea. The ossicles act mainly as a ...

16

Audio processing is a large field, but specifically in speech processing, an open database of samples known as Harvard Sentences is widely used. Harvard sentences are phonetically balanced collections of sentences in American English. Many equivalent databases exist for other languages as well. Actual files with Harvard Sentences speech can be found here.

14

What you want to do is called "Super Resolution" in the field of imaging. This is an ill posed problem. Hence in order to solve it you need some prior / model about your audio data. For example you may look at the model in Speech Super Resolution Using Parallel WaveNet.

9

You're running into a property of the DFT that is usually used in the opposite direction: stuffing zeros between samples in one domain results in replication of the entire sequence in the opposite domain. Let's start in the frequency domain with the signal you plotted, $X[k]$, and look at the inverse transform to see what time-domain signal it corresponds to....

9

While I don't know about tools for upsampling using AI, I am challenging the assumption that the main problem with your sample is lost high frequncies due to resampling from 44kHz to 22kHz, so "just" upsampling is unlikely to solve your root problem. The possibility of AI upsampling itself is sensible, though: While standard (mathematical) ...

8

As you have realized, the hard part of doing digital communications is carrier, symbol and frame synchronization, and channel estimation/equalization. The bad news is that you can't get around these problems. The good news is that implementing these is not that hard, as long as you limit yourself to narrowband BPSK. I know, because I have done this myself, ...

7

I believe that this "color graph" you are looking for is a spectrogram (although it looks to me more like a scalogram, but you did not mentioned wavelets). Let me give you an example in MATLAB of obtaining such plot: load handel nfft = 512; noverlap = 128; win = hamming(nfft); spectrogram(y, win, noverlap, nfft, Fs, 'yaxis') colormap('jet') So first line ...

7

From the ones I've been using I can recommend: YAAFE - very pleasant to work with in Python ESSENTIA - another one I like particularly due to Python integration aubio FEAPI Aquila - friend of mine used it extensively and he likes it a lot Recently I came across this paper and I believe that this should perfectly answer your question. Moffat D. et al - ...

7

In line with a previous similar question here are my suggestions: There are so many nice books but I believe you should first have a look at the science of sound from Rossing for getting the most broad view on the subject. Then you can look at the following books , each dealing with a particular dimension of the problem: 1- Acoustics_BERANEK 2- Elements ...

7

An exponentially decaying envelope $a\exp(-b x)$ is a good choice, and is used for example in vintage Yamaha FM synthesizers. It has the favorable property that over any constant length time interval, by the end of the interval the envelope has decayed to a constant fraction of what it was at the beginning of the interval. Damped oscillation (with some ...

6

The matlab codes that implemented D. Ellis's algorithm are on their website: http://labrosa.ee.columbia.edu/projects/beattrack/

6

"Ok Google" is described in many publications by Google Automatic Gain Control and Multi-style Training for Robust Small-Footprint Keyword Spotting with Deep Neural Networks Convolutional Neural Networks for Small-Footprint Keyword Spotting It is based on DNN specifically trained for keyphrase and runs really fast. It does not consume a lot of power even ...

6

The main reason why the tables don't have it, is that it's hard to measure. The measurement technique in ISO 354:2003 relies on measuring the difference in reverberation times in a reverberation rooms with and with/out a material sample. At higher frequencies, the reverb time is dominated by air absorption and and the sound field becomes less and less ...

6

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 ...

5

I also think that NIST database is very popular when it comes to speech recognition tasks. In fact it is a standard for comparison of new algorithms and techniques during yearly challenges. Additionally MATLAB's load handel containing snippet of G.F.Handel - Hallelujah is quite commonly used.

5

Eventhough the exact cause of those wide-spread line spectra is not very clear to me from the supplied information, it's most probably due to the on-off switching implied by the silence period you have added in between your message signals. The on-off waveform is an implicit operation you multiply to your signal. Which has a value of $1$ during the sound ...

5

i don't consider this a "bad" question. But there is a lot that nano needs to deal with. first, you must be able to think about conceptually and mathematically converting your continuous-time signal $$x(t) = \sin(\Omega t)$$ into a discrete-time signal $$x[n] = \sin(\omega n)$$ how $n$ is related to $t$ and how $\omega$ is related to $\Omega$. ...

5

As your plot shows, the second form allows for the correlation peak to be negative. Now, what does a strong negative cross correlation mean? It means the signals are very similar, except one has a negative sign in front of it, i.e., $x_1 \approx -x_2$. Whether or not this makes sense depends a lot on the actual application. In the application you describe, ...

5

A big question. Plz have a look at this first https://en.wikipedia.org/wiki/Sound_recording_and_reproduction. Sound is highly related to vibration. Sound is generated by vibration of a sound source, and you can hear a sound is because of the vibration of eardrum. Sound Recording Sound pressure is the local pressure deviation from the ambient (average or ...

4

All the standards define dBFS as an RMS measurement, relative to the RMS level of a full-scale sine wave, so the calculation is: value_dBFS = 20*log10(rms(signal) * sqrt(2)) = 20*log10(rms(signal)) + 3.0103 A full-scale sine wave is 0 dBFS A full-scale square wave is +3 dBFS The similar unit dBov is defined in relation to power ratios (so it's also an RMS ...

4

The European Broadcasting Union's (EBU) Sound Quality Assessment Material (SQAM) resource is pretty popular. https://tech.ebu.ch/publications/sqamcd

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What you are observing is the digital representation of the voltage, which in fact represents the acoustic pressure. Workflow would be something like: Vibrating larynx is producing Acoustic Pressure [Pa] Variations of that pressure are converted by the microphone into Voltage [V] Voltage is being sampled and quantized by the ADC (Analog to Digital Converter)...

4

When using a constant tone audio beacon, beware of room echoes causing multi-path interference and distortion, especially around the leading and trailing portions of your received waveforms. Try using a frequency sweep instead of a constant tone for your transmit waveform. This might provide you with a sharper correlation peak that is less likely to have ...

4

But which features of signals reveal differences between piano and guitar Look at things like timbre; basically, most things when excited to oscillate will not only produce a single tone, but a set of overtones, too, and those are weighted differently; also, there tends to be a different temporal "decaying" and frequency changing after excitation.

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The phase vocoder was first implemented by Flanagan and Golden[1] using analogic filters bank, later Portnoff apply the same concept digitally using FFT. The phase vocoder use the difference of successive phase spectra ∆φ[i], it will help find the Instantaneous Frequency used in phase synthesys. A little math:  q[i] = \frac{N}{2πH} princarg \left[φ_l[...

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The 2 stereo channel might be out of phase (near opposites, thus cancelling each other out when mixed to mono). The human ear-brain doesn't just sum stereo left-right to the 2 ears, but instead uses any phase differences to help determine directionality.

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In the end, I used DTMF (Dual Tone Multi Frequency signaling). The original DTMF has 16 signals each using a combination of 2 frequencies. But here I only used "1"(697 Hz and 1209 Hz) and "0" (941Hz and 1336 Hz) An outline of how the code works: The sender converts text to binary, then transmit "0" / "1" DTMF signals (here the timing is 0.3s for tone ...

4

Here is roughly how it works. A typically musical note is made out of harmonics and the fundamental. The fundamental is also the spacing between the harmonics. For 100 Hz fundamental you would get 100Hz, 200Hz, 300Hz, 400Hz, 500 Hz, etc. For 200 Hz you would get 200Hz, 400Hz, 600 Hz, which is simlar to 100Hz but not the same. Note that specifically 300Hz ...

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