# Tag Info

17

Encouraged by Hilmar, I've decided to update the answer with all the steps necessary to calculate the Reverberation Time from a scratch. Presumably, it will be useful for others interested in this area. Obviously, it is the simplest approach because more advanced are definitely beyond a scope. In the beginning, you must obtain the impulse response of a room....

9

Yes, it is called acoustic communications. Here is an example of a paper that uses orthogonal frequency division multiplexing (OFDM) in an underwater acoustic channel. EDIT: Note that you wouldn't call it a SONAR any more because SONAR stands for SOund Navigation And Ranging, whereas this is a communication system, just like you wouldn't call your cell ...

7

For a sine wave of amplitude $A$ and frequency $f=1/T$, its energy from $-T/2$ to $T/2$ is $A^2T/2$. Thus the power depends only on amplitude. The energy over one period does depend on frequency, but in the same observation duration, two sines having same $A$ should have the same energy (assuming observation duration is multiple of their periods). In ...

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

Well, there is plenty of ways for approaching this problem. Which one is best depends on room itself, your resources and pursued accuracy. I think that best way to start is to look into ISO 3382-2:2008 standard, you will find lot of information. You should remember that post processing of your signals is very important here. Nevertheless you should consider ...

6

I assume here that your device is not in the feedback chain. If you can't afford a FFT or filter-bank decomposition (and then detect over successive frames the FFT bins in which the amplitude gets almost exactly multiplied by the same complex number over successive frames), I would suggest looking at these few parameters: Fit a line to the log of the RMS ...

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

This question (about "time-scaling" audio) is closely related to pitch shifting, which is time-scaling combined with resampling. But changing the speed without changing pitch is only time-scaling, so there is no resampling involved (contrary to what thomas has suggested). There are frequency-domain methods (phase-vocoder and sinusoidal modeling) that can ...

6

Comments provided here are in two broad categories: Presentation and Subject matter. The "Presentation" section is the easiest to address. There are some things that could be rephrased in terms of language use but these might be just personal preferences. The "Subject matter" section includes comments in methodology which could take more time to address. ...

6

Yes, actually sound waves are better than RF signal in underwater, because of the low frequency requirement. We don't covert sound waves to radio signals. The transceiver in this case is called transducer. EDIT: You have basically three options for wireless communication in underwater: RF signals, acoustic signals, and optical signals. RF signals suffer ...

5

You have 3 ways of communicating underwater 1) Acoustic : Most popular means of communication. Has high latency but good range 2) Low frequency RF. To increase the range you have to lower the frequency, which means small bandwidth, so you can't transmit a lot of information. 3) Optical: Low latency + High bandwidth but the range is limited to less than ~...

5

The best way for underwater communication is to be acoustic communication where sound waves are used. Sometimes, visible light is used such as red and green, but in all cases, acoustic communication is common and used more. Underwater acoustic communication is considered as one of wireless networks types. but it's more complicated, I can say the most ...

3

It's not because of reverberation. When you want to model the Frequency Response of the room, it's common to simplify your approximation by using either all-pole or all-zero models. You don't want to use the full zero-pole model. To get some intuition: zeros correspond to time delays and antiresonances poles correspond to resonances of your Room Response ...

3

It depends on how good your interpolation between samples is. If you have really good interpolation, anything more than 2 samples per cycle will suffice. This is not just a theoretical fact, it is my experience in practice using polyphase interpolation that combines 64 adjacent table-lookup samples. If you're using linear interpolation (which combines 2 ...

3

White noise: sounds exactly the same if you play it back at double the speed Stack up diminished fifth starting at a low enough fundamental (say 20 Hz). Play back at double speed: sounds exactly the same. Stack octaves with equal amplitude starting with a low enough fundamental. Play back at twice the speed: sounds exactly the same Detune the octaves ...

3

If the channel is static and you are in a deep fade -- then, yes, your only option is to increase the bandwidth or go multicarrier like you suggest. In fact, modern WiFi systems use that exact technique: OFDM ("Orthogonal frequency division multiplexing" -- sometimes known as multi-tone or multicarrier transmission). Alternatively, you could go with two ...

3

This depends highly on the signal and it's content. For narrow band signal, the loudness can fairly well be estimated through the equal loudness curves as published in ISO 226 (see for example) http://en.wikipedia.org/wiki/Equal-loudness_contour For wide band signal, things are more complicated. If the signals are stationary, you apply frequency weighting ...

3

Your polarity inversion method simply applies the same phase shift (delay) and gain to all frequencies, and is presumably not adaptive (or even closed loop). It may work reasonably well for some limited cases, where the phase and gain have been tailored for that particular environment, but any change to the environment will tend to reduce its effectiveness. ...

3

To a first approximation, a plucked string has an exponential decay, so your envelope will look like $$x(t) = a \exp -bt.$$ The physical explanation for this is simple: when you pluck the string, you put a certain amount of energy into the system. Over time, energy is lost. At any given time, the amount of energy lost is proportional to the amount of energy ...

3

Actually, magnetic induction is a current research topic for underwater communications, see http://bwn.ece.gatech.edu/papers/2015/j14.pdf. Also, this paper includes a nice comparison of underwater communication strategies, including electromagnetic waves, acoustical communication, and optical communication. One major disadvantage of magnetic induction is ...

2

It is a little late, but i'm also working on convolution reverb at the moment. If it is still of interest, you can use my code. Simply call the function convolution_reverb and pass the paths to the two audio files (audio and impulse response, both need to be .wav files), as well as the name for the result file to be created. import numpy as np from wave ...

2

The way you read the files is a little odd. The method used here is clearer with respect to the sampling rate and the signal. You will note that the above example calls pcm2float after reading. This may be the cause of your problem. The wave reader returns an array of ints. Processing those (quite large) values may well cause problems. Try converting ...

2

By using Acoustic Echo Cancellation. Since the signal that goes to the speaker is known you can subtract it out at the microphone, provided you know the exact transfer function from speaker to microphone. The determination of the transfer function is typically done through an adaptive filter. Similar techniques can be used to reduce reflections, ...

2

I don't have a mathematical proof for you, but here are 2 issues to think about. The impulse response from the actuator (speaker) to the error measurement (microphone) is non-minimum phase. This is unavoidable because it takes a non-zero amount of time for an acoustic wave to travel from the speaker to the microphone. In a digital system there is additional ...

2

There are a few pieces of background information that help in answering this question: Geometric series and in particular geometric series as applying to music A geometric series, $x_n$, is one where each successive term is generated by multiplying the previous term by some factor. For example: $x_{n+1} = a x_n$ Geometric series are everywhere in music, ...

2

Some possible perceivable differences might include: differences in pitch frequency (bass vs. soprano voice, etc.) differences in overtone series (formants, head resonances, accent) variations in modulation of pitch over time (vibrato, tremolo, accent) differences in voiced to unvoiced ratios (raspiness?) variations in typical durations of each vowel or ...

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