I am doing sound latency test. my device will be receiving either a beep signal or a silence signal. How can i differentiate between these signals. Please help me. Thanks in advance..
Look at around 10 ms worth of samples (e.g. 441 samples at 44.1 kHz) and measure the energy in that buffer. If it's above some threshold it's a signal and if it's below the threshold then it's silence.
To measure energy just sum the squared value of each sample in the buffer and divide by the number of samples.
It depends. If the digital audio was generated synthetically (like by another function) and you can thus rely on the fact that, in one case, you'll get true digital silence (zeroed samples), then the solution is simply to test for the zeroed samples over the measurement window. Anything other than zero is not silence.
I would guess, though, that you're dealing with real-world audio recorded from, say, a microphone. If this is the case, then measuring the energy in a time window and comparing it to a threshold indeed makes sense. The two parameters that you'll have to determine are:
Threshold energy level
Length of the time window
If the threshold is too low, your false positive rate will be too high; background noise that is not a beep may be interpreted as a beep. Conversely, if your threshold is too high, your system could categorize a beep as noise. Luckily, if you're doing audio with a reasonably low background noise, your performance won't be very sensitive to this threshold.
Longer window lengths will decrease these false positive/negative rates, thus making your system more robust, but system usability may suffer with overly long windows. For instance, automated phone systems classify keypresses to aid menu navigation. If they required the user to hold each key for three seconds at a time, the accuracy would improve but at the expense of almost all usability.
I encourage you to NOT make a decision based solely on the one maximal sample as Paul suggested. Doing this completely undermines the resistance to false positives provided by the length of the sampling window.
What if they use the loop back method, does noise take into account? For example, If they send a Beep to second device, Loopback & send it back to the sender, send a silence packet and do the same, Can't they measure the latency at the sender level(provided they know the actual network latency).
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I'm doing a project studying rats who squeak in the ultrasonic range (20kHz to 100kHz) using Matlab software and sound files.
I have (or will be getting) a couple .wav audio signals of these rats speaking, and among general analysis of these wave forms, I also want to convert these ultrasonic signals (outside of our hearing), into the human audible range (20hz to 20khz).
Could I get some advice on how to do this conversion (via Matlab programming and not by using equipment)
Looking into this, I've found names such as:
-frequency division
-heterodyning
-envelope detection
-time expansion
but looking into these it seems either they are explained in terms of what the equipment (bat detectors) does, or they sound incredibly similar to each other. e.g. frequency division and time expansion both involve dividing the incoming signal by 10
since I am looking into what seems to be unfamiliar turf, it would be great to find multiple ways to convert the signal (to my knowledge the names above have their own associated positive and negative traits)
Your question is a signal processing question more than a Matlab question, which isn't really what Stack Overflow is about, so you might get some negative votes.
There are indeed a number of methods of changing the frequency of audio (or any signals):
1) Slow it Down: The least disruptive to the signal is simply to slow down the audio. If you are looking to have rat signals up to 100 kHz, you'll need to sample the audio at 200 kHz or greater. Once you have your recording, simply re-save the wav file telling it that the sample rate is 44.1 kHz (or whatever). This will play it more slowly, but all the frequencies will now be audible (unlike the single side band demodulation discussed below). This is definitely the place you should start...it's the easiest and will sound the best.
fs = 200e3; %your original sample rate
myAudio = load('myFile.mat'); %your original audio
fs = 44.1e3; %simply declare that you want a lower sample rate
wavwrite(myAudio,fs,16,'myFile_44kHz.wav'); %save it out at the new rate
2) Single-Side Band: Use the demod command to "demodulate" the signal to lower its frequency. There are a number of demodulation methods available with this command. I'd use "single side band (suppressed carrier)" because that is how the rat itself (and humans) create sound. To do the demodulation, you'll have to assume a "carrier frequency", as if it were a radio signal. If the lowest frequency of a rat squeek is 20 kHz, you can assume a carrier of 20 kHz. This operation will shift all of your audio down by 20 kHz. As a result the squeek that was originall 20-100 kHz, will now be 0-80 kHz. So, you won't hear the whole thing, but you'll hear part of it.
fs = 200e3; %your original sample rate
myAudio = load('myFile.mat'); %your original audio
[b,a]=butter(2,20e3/(fs/2),'high'); %define highpass filter
myAudio = filtfilt(b,a,myAudio); %remove the low frequencies
myAudio = demod(myAudio,20e3,fs,'amssb'); %shift it down 20 kHz
wavwrite(myAudio,fs,16,'myWave_shifted.wav'); %save it out
3) Phase Vocoder (or other Pitch Shifting): To hear the whole 20-100 kHz range (which is 80 kHz bandwidth, which is 4x bigger than the 20 kHz bandwidth of human hearing), you've got to go to more extreme methods. These methods will make the audio sound bizarre, but you can give it a try. There are several algorithms. Look up "phase vocoder". Or, use one of audio processing software packages like Audacity, Raven, etc.
I currently work in an experimental rock mechanics lab, and when I conduct an experiment, I record the output signals such as effective torque, normal force and motor velocity. However, the latter quantity causes significant cross-talk over the recorded channels, and I want to filter this out. Let me give an example:
Here the upper plot is the strong signal (motor velocity), and the lower is an idle signal that is affected by the cross-talk (blue is raw signal, red is median filtered). The idle channel is only recording noise. We see three effects here. When the motor voltage changes:
the amplitude of the noise increases
the idle signal's median shifts
there is a spike that lasts approximately 0.1 seconds
If we zoom in on the first spike that occurs at around 115 seconds, we get the following plot. This does not seem to be your typical delta-function type of spike, but rather some kind of electronic "echo".
I have seen much work on blind source separation through independent component analysis (ICA), but that did not prove to be effective in my situation. However, since I know the shape of the signal that is causing the cross-talk, there may be better ways to include this information. My question is this: is there a filter or a combination of filters that can tackle the effects mentioned above?
As I am a geologist and not an electrician or mathematician, I don't have a proper background for this kind of material, so please bear with me. I write Python, MATLAB and C++ quite well, so suggested algorithms written in any of those languages is preferred (but not required).
The crosstalk you encounter, results from a parasitic transmission line. Just think of your typical FM-receiver - where the wires equal the antennae. These effects include parasitic and inductive coupling, and form an oscillator (which is the reason, why you cannot see, the theoretically ideal delta spike)
I recognize two different approaches:
use a hardware filtering circuit
use a software-implemented filter
ad 1:
depending on the needed bandwith (maximum frequency/rate of change) on the idle channel, you can determine the corner frequency, as well as the required filter-order, for a given rate of suppression
ad 2:
you can implement several types of filters (IIF, FIR) which resemble these circuits.
Additionally, if you are measuring the aggressive signal anyways, you can use the measurement on the idle channel to determine system-parameters for a mathematical model of the crosstalk. With this model you'd be able, to exclude the interference by calculation
Is this because it's a complex problem ? I mean to wide and therefore it does not exist a simple / generic solution ?
Because every (almost) software making signal processing (Avisoft, GoldWave, Audacity…) have this function that reduce background noise of a signal. Usually it uses FFT. But I can't find a function (already implemented) in Matlab that allows us to do the same ? Is the right way to make it manually then ?
Thanks.
The common audio noise reduction approaches built-in to things like Audacity are based around spectral subtraction, which estimates the level of steady background noise in the Fourier transform magnitude domain, then removes that much energy from every frame, leaving energy only where the signal "pokes above" this noise floor.
You can find many implementations of spectral subtraction for Matlab; this one is highly rated on Matlab File Exchange:
http://www.mathworks.com/matlabcentral/fileexchange/7675-boll-spectral-subtraction
The question is, what kind of noise reduction are you looking for? There is no one solution that fits all needs. Here are a few approaches:
Low-pass filtering the signal reduces noise but also removes the high-frequency components of the signal. For some applications this is perfectly acceptable. There are lots of low-pass filter functions and Matlab helps you apply plenty of them. Some knowledge of how digital filters work is required. I'm not going into it here; if you want more details consider asking a more focused question.
An approach suitable for many situations is using a noise gate: simply attenuate the signal whenever its RMS level goes below a certain threshold, for instance. In other words, this kills quiet parts of the audio dead. You'll retain the noise in the more active parts of the signal, though, and if you have a lot of dynamics in the actual signal you'll get rid of some signal, too. This tends to work well for, say, slightly noisy speech samples, but not so well for very noisy recordings of classical music. I don't know whether Matlab has a function for this.
Some approaches involve making a "fingerprint" of the noise and then removing that throughout the signal. It tends to make the result sound strange, though, and in any case this is probably sufficiently complex and domain-specific that it belongs in an audio-specific tool and not in a rather general math/DSP system.
Reducing noise requires making some assumptions about the type of noise and the type of signal, and how they are different. Audio processors typically assume (correctly or incorrectly) something like that the audio is speech or music, and that the noise is typical recording session background hiss, A/C power hum, or vinyl record pops.
Matlab is for general use (microwave radio, data comm, subsonic earthquakes, heartbeats, etc.), and thus can make no such assumptions.
matlab is no exactly an audio processor. you have to implement your own filter. you will have to design your filter correctly, according to what you want.
I'm hoping someone will be able to tell me why no filtering is helping in my application.
I have a MEMS microphone monitoring the pressure of a small chamber, which has a membrane stretched over the far end. This device is placed on a human muscle and when I flex said muscle the membrane is disturbed, producing a pressure difference in the chamber, which the microphone picks up. Therefore, by flexing a muscle I can see nice spikes of activity. However, this method is very susceptible to noise, both motion artefacts and other undesirable artefacts.
The muscle activity I'm interested in is above 10Hz and below 100Hz, so I'm trying to bandpass (or at the very least, highpass) the noise. If I tap the device, or if I have the device on my upper forearm and tap my wrist, I'm to understand that this is a very low frequency noise, somewhere in the region of 1Hz/2Hz, but I can't get rid of this noise!
I'm using MATLAB to process. Generally I sample this microphone at 1KHz, but I currently have it hooked up to a DAQ at 5KHz sampling rate. I desperately want to get rid of this low frequency noise but nothing I try seems to make any difference, it's very hard to see what the filter is doing at all. It's definitely attenuating the signal, but not getting rid of the noise I want. I don't expect perfect results, but certainly better than what I'm seeing.
I've used lots of methods to create filters in MATLAB (manually and fdatool), along with different types of filters (Butterworth, Chebyshev, Elliptic) all not helping. I'm worried that my desired frequency of 10Hz is perhaps too close to the noise I'm trying to filter out, and it's not able to attenuate the noise enough.
Any ideas, code samples, or recommendations would be very helpful.
Tapping or percussive sounds are broad spectrum, producing frequency content well above the repeat rate of 1 Hz or so. So any linear band pass or high pass filter will not be able to completely remove this broad spectrum noise.
I think shopkick is detecting very high frequency signal which is not audible to human ear.But the real question is how they can detect signal of more than 22khz in iphone. I have checked frequency response of iphone mic,it seems to be from 20 hz to 22 khz within the human audible range.
http://blog.faberacoustical.com/2009/iphone/iphone-microphone-frequency-response-comparison/ http://www.businessinsider.com/shopkick-crate-barrel-2010-12?op=1
Can you guide me on this. If it is possible with iphone mic,then we can able do some signal processing specifically FFT in order to get frequency.
Well I am currently working on a similar system of transmitting data using these high frequencies and this is what I found out. Al-thou keep in mind that I am doing this with Android phones, mostly Galaxy S line.
First of all spectrum of 20khz to 22khz seems quite promising because it can be detected by all phones we tested and even reproduced by some of them. These frequencies are inaudible to humans of any age and even the dogs and cats seem to not notice them. If you are targeting (actually avoiding) detection by humans you could even go to as low as 18khz since most people wouldn't hear that. This gives you a bandwidth of 4000hz which you can Frequency modulate a data into. Of course don't expect to transmit 8mp images but some small data can be transmitted. You are right in the part that you could than use FFT to transit into frequency domain and analyse those frequencies, this can be done even on older phones in Java (I think doing it in objective c would be even faster).
Also if you have few iPhones on your disposal you could install any frequency analyser and play the frequencies you want on another iPhone or some speaker to test what they can detect. Just keep in mind that standard desktop speakers would probably be able to play the given frequencies but will introduce noise of lower frequency. Piezo tweeters are probably best for these type of sounds al-thou I must say I am using iPhone 4 to play these frequencies for testing quete efficiently.
I read somewhere that Shopkick now even plays there sound codes over stores PA-s and since those speakers are not really optimised for above 20khz response I too am starting to suspect they are using frequencies below that. Take a look at this website for different store codes that some people are using to cheat the system http://www.ceploitips.com/2011/03/shopkick-walk-in-files.html
Keep in mind that using these might ban your account since they improved there misuse detection algorithms.
Also I too would like to read more about the Shopkick implementation so if anyone viewing this has some link please share.
First, human hearing pretty much tops out at 20 KHz and even that requires a very young human and a very low and erratic shift along those upper frequencies. For example, I can produce a tone as low as 18 KHz at full iPad volume at a sample rate of 48 KHz that even my dog doesn't notice. Read up on PsychoAcoustics and you will see that humans filter echoes at even very low frequencies that are there but we don't notice them.
But in the case of ShopKick, I don't think they are going above even 21 KHz. I have created several digital audio modulations on the iPhone and 21 KHz seems to be the upper limit for any distance at all.
It would help if you gave more input on what you are doing. I assume from the question you want to modulate a digital signal between two devices.
My best guess is that they are using maximal length sequences. These are almost like a weak background hiss that covers a large range of the audio spectrum. The key to detection is that the pattern repeats exactly and the phone has a key that detects the sound by correlating the key and the incoming audio.