One part of an app I'm currently working on will work as a tuner. I want to be able to use the iPhone to display the peak frequency of a signal given by the user. I have used the SCListener which worked very good on the iPhone simulator. However when I tried it on a real device it didn't.
Forums suggests that I use apple FFT and accelerate Framework to do this but it seems overly complicated. I would really appreciate if anyone that has programmed a tuner or similar could point me in a good direction!
Thanks!
There is a related post on dsp.stackexchange. It suggests that autocorrelation will work better than FFT at finding the fundamental, if the fundamental is lower in amplitude than the harmonics. Autocorrelation is slightly less tricky than FFT. The accelerate framework will come to your help there again for that. However this is not the case usually.
I don't know of any out of the box solutions which will do all the work for you. The vDSP Programming Guide has specific worked examples for real FFTs which you might want to look into, it takes some getting used to, but it's worth it really. FFT seems like the most logical first step in peak frequency extraction I'm afraid. Most sources seem also to suggest that applying a windowing function to the time domain signal before running the FFT is critical (or you will get high frequency artifacts because of discontinuities at the extremities).
Also you might want to check out this related SO post.
Peak frequency is often different from the pitch frequency that one would want a (music) tuner to estimate. Look up pitch estimation.
From previous experience doing this :
FFT isn't as always as accurate as you might think, and is computationally expensive
Autocorrelation gives pretty good results
If you have a strong fundamental, zero-crossing can be very accurate and is very computationally efficient (just count the number of times the signal crosses zero over a period of time, f = (2 x time period in seconds)/(number of zero crossings)\
Hope that helps.
Thanks for all the answers! I had missed a part in my code to make the SC listener work on the device as well but are now trying to change it for Apples own AVAudioRecorder since it is suppose to be a lot faster. The problem was that the cocos2d framework blocked the recording of sounds until you called for a method that allowed this. It works like a charm now! :)
Thanks again!
Related
I am using an STM accelerometer with STM controller.
While getting the readings from the accelerometer, it gives a random value. It even shows a false value, when used in a steady position.
Here, somehow, I am facing problem and need some idea and suggestion to solve this issue. If some one has the document and sample code for it, please, let me know and help me.
There are several potential issues. The accelerometer could need to be calibrated. Often they will not start at zero when at rest straight from the factory (I have used other accelerometers but not the particular one you are using so I can't say if they are pre-calibrated).
When you say "false values" depending on the magnitude this could also be noise. Accelerometers are prone to being noisy, so you ideally you would want to low-pass filter the data you collect to reduce noise. The cutoff frequency you select depends on the particular application and your sampling rate.
I want to know the frequency of data. I had a little bit idea that it can be done using FFT, but I am not sure how to do it. Once I passed the entire data to FFT, then it is giving me 2 peaks, but how can I get the frequency?
Thanks a lot in advance.
Have a look at this page for an explanation on how to calculate it:
FFT Fundamentals
Please also check this answer (it's C# code but I think you can easily understand it)
How do I obtain the frequencies of each value in an FFT?
And finally have a look at this one, it uses DFT instead of FFT:
Determining the magnitude of a certain frequency on the iPhone
I also found this implementation that you can use in Objective-C:
A lib to find the frequency https://github.com/jkells/sc_listener
A example using the above library https://github.com/jkells/sc_listener_sample
Regards
An FFT will give you the frequency of all the sinusoidal components of a signal. If instead you want just the frequency of the periodicity of common waveforms (more interesting sounding and looking that a plain sinewave) such as produced by speech and music, then you may want to use a pitch detection/estimation algorithm instead of just an FFT peak.
I'm looking for some functions in MATLAB in order to find out some parameters of sound,such az intensity,density,frequency,time and spectral identity.
i know how to use 'audiorecorder' as a function to record the sampled voice,and also 'getaudio', in order to plot it.But i need to realize the parametres of a sampled recorded voice,that i mentioned above.i'd be so thankful if anyone could help me.
This is a very vague question, you may want to narrow it down (at first) and to add as much contextual details as you can, it will certainly attract a lot more answers (also as mentionned by Ion, you could post it at http://dsp.stackexchange.com).
Sound intensity: microphones usually measures pressure, but you can get the intensity from that quite easily (see this question). Your main problem is that microphones are not usually calibrated, this means that you cannot associate an amplitude with a pressure. You can get sound density from sound intensity.
Frequency: you can get the spectrum of your sound by using the Fast Fourier Transform (see the Matlab function fft).
As for spectral or time identity, I believe these are psychoacoustics notions, which is not really my area of expertise.
I'm no expert but I have played with Matlab a little in the past.
One function I remember was wavread() to input a sound signal into Matlab, which if executed in this form [Y, FS, NBITS]=WAVREAD("AUDIO.WAV") would return something like:
AUDIO.WAV:
Fs = 100 kHz
Bits per sample = 10
Size = 100000
(numbers from the top of my head)
Now about the other things you ask, I'm not really sure. You can expect a better answer from somebody else. I think this question should be moved to Signal Processing SE btw.
I'm trying to write a simple tuner (no, not to make yet another tuner app), and am looking at the AurioTouch sample source (has anyone tried to comment this code??).
My worry is that aurioTouch doesn't seem to actually work very well when looking at the frequency domain graph. I play a single note on an instrument and I don't see a nicely ordered, small, set of frequencies with one string peak at the appropriate frequency of the note.
Has anyone used aurioTouch enough to know whether the underlying code is functional or whether it is just a crude sample?
Other options I have are to use FFTW or KISS FFT. Anyone have any experience with those?
Thanks.
You're expecting the wrong thing!!
Not the library's fault
Whether the library produces it properly or not, you're looking for a pattern that rarely actually exists in real-life sounds. Only a perfect sine wave, electronically generated, will cause an even partway discrete appearing 'spike' in the freq. graph. If you don't believe it try firing up a 'spectrum analyzer' visualization in winamp or media player. It's not so much the PC's fault.
Real sound waves are complicated animals
Picture a sawtooth or sqaure wave in your mind's eye. those sharp turnaround - corners or points on the wave, look like tons of higher harmonics to the FFT or even a real fourier. And if you've ever seen a real 'sqaure wave/sawtooth' on a scope, or even a 'sine wave' produced by an instrument that is supposed to produce a sinewave, take a look at all the sharp nooks and crannies in just ONE note (if you don't have a scope just zoom way in on the wave in audacity - the more you zoom, the higher notes you're looking at). Yep, those deviations all count as frequencies.
It's hard to tell the difference between one note and a whole orchestra sometimes in a spectrum analysis.
But I hear single notes!
So how does the ear do it? It considers the entire waveform. Then your lower brain lies to your upper brain about what the input is: one note, not a mess of overtones.
You can't do it as fully, but you can approximate it via 'training.'
Approximation: building some smarts
PLAY the note on the instrument and 'save' the frequency graph. Do this for notes in several frequency ranges, or better yet all notes.
Then interpolate the notes to fill in gaps (by 1/2 or 1/4 steps) by multiplying the saved graphs for that instrument by 2^(1/12) (or 1/24 for 1/4 steps, etc).
Figure out how to store them in a quickly-searchable data structure like a BST or trie. Only it would have to return a 'how close is this' score. It would have to identify the match via proportions of frequencies as well, in case it came in different volumes.
Using the smarts
Next time you're looking for a note from that instrument, just take the 'heard' freq graph and find it in that data structure. You can record several instruments that make different waveforms and search for them too. If there are background sounds or multiple notes, take the closest match. Then if you want to identify other notes, 'subtract' the found frequency pattern from the sampled one, and rinse, lather repeat.
It won't work by your voice...
If you ever tried to tune yourself by singing into a guitar tuner, you'll know that tuners arent that clever. Of course some instruments (voice esp) really float around the pitch and generate an ever-evolving waveform (even without somebody singing).
What are you trying to accomplish?
You would not have to totally get this fancy for a 'simple' tuner app, but if you're not making just another tuner app them I'm guessing you actually want to identify notes (e.g., maybe you want to autogenerate midi files from songs on the radio ;-)
Good luck. I hope you find a library that does all this junk instead of having to roll your own.
Edit 2017
Note this webpage: http://www.feilding.net/sfuad/musi3012-01/html/lectures/015_instruments_II.htm
Well down the page, there are spectrum analyses of various organ pipes. There are many, many overtones. These are possible to detect - with enough work - if you 'train' your app with them first (just like telling a kid, 'this is what a clarinet sounds like...')
aurioTouch looks weird because the frequency axis is on a linear scale. It's very difficult to interpret FFT output when the x-axis is anything other than a logarithmic scale (traditionally log2).
If you can't use aurioTouch's integer-FFT, check out my library:
http://github.com/alexbw/iPhoneFFT
It uses double-precision, has support for multiple window types, and implements Welch's method (which should give you more stable spectra when viewed over time).
#zaph, the FFT does compute a true Discrete Fourier Transform. It is simply an efficient algorithm that takes advantage of the bit-wise representation of digital signals.
FFTs use frequency bins and the bin frequency width is based on the FFT parameters. To find a frequency you will need to record it sampled at a rate at least twice the highest frequency present in the sample. Then find the time between the cycles. If it is not a pure frequency this will of course be harder.
I am using Ooura FFT to compute the FFT of acceleromter data. I do not always obtain the correct spectrum. For some reason, Ooura FFT produces completely wrong results with spectral magnitudes of the order 10^200 across all frequencies.
I'm trying to write a simple tuner (no, not to make yet another tuner app), and am looking at the AurioTouch sample source (has anyone tried to comment this code??).
My worry is that aurioTouch doesn't seem to actually work very well when looking at the frequency domain graph. I play a single note on an instrument and I don't see a nicely ordered, small, set of frequencies with one string peak at the appropriate frequency of the note.
Has anyone used aurioTouch enough to know whether the underlying code is functional or whether it is just a crude sample?
Other options I have are to use FFTW or KISS FFT. Anyone have any experience with those?
Thanks.
You're expecting the wrong thing!!
Not the library's fault
Whether the library produces it properly or not, you're looking for a pattern that rarely actually exists in real-life sounds. Only a perfect sine wave, electronically generated, will cause an even partway discrete appearing 'spike' in the freq. graph. If you don't believe it try firing up a 'spectrum analyzer' visualization in winamp or media player. It's not so much the PC's fault.
Real sound waves are complicated animals
Picture a sawtooth or sqaure wave in your mind's eye. those sharp turnaround - corners or points on the wave, look like tons of higher harmonics to the FFT or even a real fourier. And if you've ever seen a real 'sqaure wave/sawtooth' on a scope, or even a 'sine wave' produced by an instrument that is supposed to produce a sinewave, take a look at all the sharp nooks and crannies in just ONE note (if you don't have a scope just zoom way in on the wave in audacity - the more you zoom, the higher notes you're looking at). Yep, those deviations all count as frequencies.
It's hard to tell the difference between one note and a whole orchestra sometimes in a spectrum analysis.
But I hear single notes!
So how does the ear do it? It considers the entire waveform. Then your lower brain lies to your upper brain about what the input is: one note, not a mess of overtones.
You can't do it as fully, but you can approximate it via 'training.'
Approximation: building some smarts
PLAY the note on the instrument and 'save' the frequency graph. Do this for notes in several frequency ranges, or better yet all notes.
Then interpolate the notes to fill in gaps (by 1/2 or 1/4 steps) by multiplying the saved graphs for that instrument by 2^(1/12) (or 1/24 for 1/4 steps, etc).
Figure out how to store them in a quickly-searchable data structure like a BST or trie. Only it would have to return a 'how close is this' score. It would have to identify the match via proportions of frequencies as well, in case it came in different volumes.
Using the smarts
Next time you're looking for a note from that instrument, just take the 'heard' freq graph and find it in that data structure. You can record several instruments that make different waveforms and search for them too. If there are background sounds or multiple notes, take the closest match. Then if you want to identify other notes, 'subtract' the found frequency pattern from the sampled one, and rinse, lather repeat.
It won't work by your voice...
If you ever tried to tune yourself by singing into a guitar tuner, you'll know that tuners arent that clever. Of course some instruments (voice esp) really float around the pitch and generate an ever-evolving waveform (even without somebody singing).
What are you trying to accomplish?
You would not have to totally get this fancy for a 'simple' tuner app, but if you're not making just another tuner app them I'm guessing you actually want to identify notes (e.g., maybe you want to autogenerate midi files from songs on the radio ;-)
Good luck. I hope you find a library that does all this junk instead of having to roll your own.
Edit 2017
Note this webpage: http://www.feilding.net/sfuad/musi3012-01/html/lectures/015_instruments_II.htm
Well down the page, there are spectrum analyses of various organ pipes. There are many, many overtones. These are possible to detect - with enough work - if you 'train' your app with them first (just like telling a kid, 'this is what a clarinet sounds like...')
aurioTouch looks weird because the frequency axis is on a linear scale. It's very difficult to interpret FFT output when the x-axis is anything other than a logarithmic scale (traditionally log2).
If you can't use aurioTouch's integer-FFT, check out my library:
http://github.com/alexbw/iPhoneFFT
It uses double-precision, has support for multiple window types, and implements Welch's method (which should give you more stable spectra when viewed over time).
#zaph, the FFT does compute a true Discrete Fourier Transform. It is simply an efficient algorithm that takes advantage of the bit-wise representation of digital signals.
FFTs use frequency bins and the bin frequency width is based on the FFT parameters. To find a frequency you will need to record it sampled at a rate at least twice the highest frequency present in the sample. Then find the time between the cycles. If it is not a pure frequency this will of course be harder.
I am using Ooura FFT to compute the FFT of acceleromter data. I do not always obtain the correct spectrum. For some reason, Ooura FFT produces completely wrong results with spectral magnitudes of the order 10^200 across all frequencies.