I'm doing an app where I want to detect sound frequency. How to detect frequency for particular sound like dog sound? Does anybody have tutorial or some sample codes?
Detecting a single frequency, or even computing a single FFT, is not a reliable method for differentiating a dog bark from other common sounds of around the same volume.
What might work is sound fingerprint analysis using MFCC's, followed by statistical pattern matching against a large enough "dog" sound database. Some pointers to the type of signal processing required might be answered here: Music Recognition and Signal Processing
This is non-trivial stuff more suited for multiple college textbook chapters than any short tutorial.
To detect the frequency, you can use a pitch detection algorithm like FFT.
Learn more here: http://en.wikipedia.org/wiki/Pitch_detection_algorithm
You can look at this project for working source code for iOS that uses FFT algorithm to detect frequencies:
https://github.com/hollance/SimonSings
Related
I'm making an app on the iphone and I need a way of detecting the tune of the sounds coming in through the microphone. (I.e. A#, G, C♭, etc.)
I assumed I'd use AVAudio but I really don't know and I can't find anything in the documentation..
Any help?
Musical notes are nothing more than specific frequencies of sound. You will need a way to analyze all of the frequencies in your input signal, and then find a way to isolate the individual notes.
Finding frequencies in an audio signal is done using the Fast Fourier Transform (FFT). There is plenty of source code available online to compute the FFT from an audio signal. In particular, oScope offers an open-source solution for the iPhone.
Edit: Pitch detection seems to be the technical name for what you are trying to do. The answers to a similar question here on SO may be of use.
There's nothing built-in to the iOS APIs for musical pitch estimation. You will have to code your own DSP function. The FFTs in the Accelerate framework will give you spectral frequency information from a PCM sampled waveform, but frequency is different from psycho-perceptual pitch.
There are a bunch of good and bad ways to estimate frequency and pitch. I have a long partial list of various estimation methods on my DSP resources web page.
You can look at Apple's aurioTouch sample app for an example of getting iOS device audio input and displaying it's frequency spectrum.
Like #e.James said, you are looking to find the pitch of a note, its called Pitch Detection. There are a ton of resources at CCRMA, Stanford University for what you are looking for. Just google for Pitch Detection and you will see a brilliant collection of algorithms. As far as wanting to find the FFT of blocks of Audio Samples, you could use the built-in FFT function of the Accelerate Framework (see this and this) or use the MoMu toolkit. Using MoMu has the benefit of it's functions decomposing the audio stream into samples for you and easy application of the FFT using it's own functions.
I am trying to write an iPhone App that should monitor the any incoming sound. I am not sure how can I get the sound recorded by iPhone's Microphone and detect its frequency. If same frequency sound repeated couple of times then I need to take some action. Could anyone please help me here. I went through the How to detect sound frequency / pitch on an iPhone? but I couldn't understood how to use them.
Any documentation or example would be really useful.
Thanks.
You'll appreciate reading this, on how to get the sound "without having to drop down to C", by using AVAudioRecorder...
Then, begin researching FFT...
Checkout this post about FFT for iPhone, which mentions various options, including the possibility of using Apple's Accelerate framework (in which you will need to drop to C) to apparently get "Apple-written FFT functions".
This is probably what you really want to read.
I am working on a program that needs to capture the frequency of sound from a guitar. I have modified the aurioTouch example to output the frequency by using the frequency with the highest magnitude. It works ok for high notes but is very inaccurate on the lower strings. I believe it is due to overtones. I researched ways on how to solve this problem such as Cepstrum Analysis but I am lost on how to implement this within the example code as it is unclear and hard to follow without comments. any help would be greatly appreciated, thanks!
As you have discovered, musical pitch is not the same as peak frequency.
But trying to investigate algorithms while trying to work with real-time audio is not easy.
I suggest you separate the problems. Record some music sounds (guitar plucks, etc.) on your Mac into raw sound files. Try your chosen pitch estimation algorithms on these recorded sample sets. Then, after you get this working, figure out how to integrate your code into the iOS audio and Accelerate (for FFT) frameworks.
I am new to Core Audio and really lost, I am trying to record an audio and then apply voice modulation to that recording and play it back. I have looked at the example Speak Here which uses Audio Queue for audio recording. I am stuck at the part of how to change the audio samples. I understand that it can be done using Audio Unit in the call back function to change the audio samples, but I have no idea what to apply to those samples to change them (will changing pitch help ?).
If you could direct me to some source code or tutorial or any site that explains voice modulation for objective C will really really help me. Thank you all in advance.
What you are trying to do here is not that simple. Basically, you would have to implement a vocoder ("voice-coder") to change a voice. The Wikipedia links should help you there.
Then, you still have manipulate those samples in CoreAudio. You can do this using Audio Queue Services but that not exactly an easy-to-use API. It might actually be less trouble to use one of the simpler CoreAudio APIs and wrap your vocoder in an Audio Unit.
Do you have some experience with audio processing? Implementing a vocoder without some prior knowledge about audio processing in general is a tough task.
First, to actually answer your question: When you called the AudioQueueNewInput() function, you pass it the name of a routine that will be called every time data is available to you. You probably called it MyInputBufferHandler() or something. It's third argument is an AudioQueueBufferRef which hold the incoming data.
Be aware that this is not as simple as looking at each sample (amplitude) and lowering or raising it. You receive samples in the temporal (time) domain as amplitudes. There is no pitch or frequency information available. What you need to do is move the incoming samples (waveform) into the frequency domain, wherein each "point" in that space is a frequency and it's accompanying power and phase. You can do that with an FFT (fast Fourier transform) but the mathematics are somewhat sophisticated. Apple does provide FFT routines in the Acceleration framework, but be aware that you are wading into very deep water here.
I want to analyze MIC audio on an ongoing basis (not just a snipper or prerecorded sample), and display frequency graph and filter out certain aspects of the audio. Is the iPhone powerful enough for that? I suspect the answer is a yes, given the Google and iPhone voice recognition, Shazaam and other music recognition apps, and guitar tuner apps out there. However, I don't know what limitations I'll have to deal with.
Anyone play around with this area?
Apple's sample code aurioTouch has a FFT implementation.
The apps that I've seen do some sort of music/voice recognition need an internet connection, so it's highly likely that these just so some sort of feature calculation on the audio and send these features via http to do the recognition on the server.
In any case, frequency graphs and filtering have been done before on lesser CPUs a dozen years ago. The iPhone should be no problem.
"Fast enough" may be a function of your (or your customer's) expectations on how much frequency resolution you are looking for and your base sample rate.
An N-point FFT is on the order of N*log2(N) computations, so if you don't have enough MIPS, reducing N is a potential area of concession for you.
In many applications, sample rate is a non-negotiable, but if it was, this would be another possibility.
I made an app that calculates the FFT live
http://www.itunes.com/apps/oscope
You can find my code for the FFT on GitHub (although it's a little rough)
http://github.com/alexbw/iPhoneFFT
Apple's new iPhone OS 4.0 SDK allows for built-in computation of the FFT with the "Accelerate" library, so I'd definitely start working with the new OS if it's a central part of your app's functionality.
You cant just port FFT code written in C into your app...there is the thumb compiler option that complicates floating point arithmetic. You need to put it in arm mode