I am looking for scaling a PNG file according to an audio provided, a frequency range (20hz-1000hz for example) and a threshold, for a smooth effect.
For example, when there is a kick, scale go to 120% smoothly, I would like to make those audio visualizers such as dubstep, etc... where when kicks comes in, their image are "pumping".
First, is it doable with ffmpeg?
Where to start?
I found showcqt that takes frequencies in input etc., but its output is a video so I don't think I can use it in my case. Any help appreciated.
If you are able to read the PCM values as they are being output, then you might consider using a rolling RMS average in order to get a continuous stream of amplitudes. IDK the best length of the array. Perhaps it should correspond to the number of audio frames that would give you an update for each visual frame? The folks at the DSP site would have the best insights.
If you do a rolling average, computations are not terribly expensive. You'd do the square on the incoming and add that to a ring buffer (circular queue) and drop the outgoing. Only those data points need be added to the rolling average when computing the new rolling average, since the denominator is fixed and known. I found a video that describes the basic RMS math here using Matlab.
It might be necessary to add some smoothing to visualizer that is receiving the volume updates. Also, handing off data from the audio thread should likely employ some form of loose coupling. It would not be good if the thread that is processing the audio was also handling graphics.
I'm a little over my head, but I think this is what is generally done for visualizers.
I've been hunting all over the web for material about vocoder or autotune, but haven't got any satisfactory answers. Could someone in a simple way please explain how do you autotune a given sound file using a carrier sound file?
(I'm familiar with ffts, windowing, overlap etc., I just don't get the what do we do when we have the ffts of the carrier and the original sound file which has to be modulated)
EDIT: After looking around a bit more, I finally got to know exactly what I was looking for -- a channel vocoder. The way it works is, it takes two inputs, one a voice signal and the other a musical signal rich in frequency. The musical signal is modulated by the envelope of the voice signal, and the output signal sounds like the voice singing in the musical tone.
Thanks for your help!
Using a phase vocoder to adjust pitch is basically pitch estimation plus interpolation in the frequency domain.
A phase vocoder reconstruction method might resample the frequency spectrum at, potentially, a new FFT bin spacing to shift all the frequencies up or down by some ratio. The phase vocoder algorithm additionally uses information shared between adjacent FFT frames to make sure this interpolation result can create continuous waveforms across frame boundaries. e.g. it adjusts the phases of the interpolation results to make sure that successive sinewave reconstructions are continuous rather than having breaks or discontinuities or phase cancellations between frames.
How much to shift the spectrum up or down is determined by pitch estimation, and calculating the ratio between the estimated pitch of the source and that of the target pitch. Again, phase vocoders use information about any phase differences between FFT frames to help better estimate pitch. This is possible by using more a bit more global information than is available from a single local FFT frame.
Of course, this frequency and phase changing can smear out transient detail and cause various other distortions, so actual phase vocoder products may additionally do all kinds of custom (often proprietary) special case tricks to try and fix some of these problems.
The first step is pitch detection. There are a number of pitch detection algorithms, introduced briefly in wikipedia: http://en.wikipedia.org/wiki/Pitch_detection_algorithm
Pitch detection can be implemented in either frequency domain or time domain. Various techniques in both domains exist with various properties (latency, quality, etc.) In the F domain, it is important to realize that a naive approach is very limiting because of the time/frequency trade-off. You can get around this limitation, but it takes work.
Once you've identified the pitch, you compare it with a desired pitch and determine how much you need to actually pitch shift.
Last step is pitch shifting, which, like pitch detection, can be done in the T or F domain. The "phase vocoder" method other folks mentioned is the F domain method. T domain methods include (in increasing order of quality) OLA, SOLA and PSOLA, some of which you can read about here: http://www.scribd.com/doc/67053489/60/Synchronous-Overlap-and-Add-SOLA
Basically you do an FFT, then in the frequency domain you move the signals to the nearest perfect semitone pitch.
I'd like to extract the pitch from a singing voice. The track in question contains only a single voice and no other sounds.
I want to know the loudness and perceived pitch frequency at a given point in time. So something like the following:
0.0sec 400Hz -20dB
0.1sec 401Hz -9dB
0.2sec 403Hz -10dB
0.3sec 403Hz -10dB
0.4sec 404Hz -11dB
0.5sec 406Hz -13dB
0.6sec 410Hz -15dB
0.7sec 411Hz -16dB
0.8sec 409Hz -20dB
0.9sec 407Hz -24dB
1.0sec 402Hz -34dB
How might I achieve such an output? I'm interested in slight changes in frequency as apposed to a specific note value. I have some DSP knowledge and I can program in C++ and python but I'd like to avoid reinventing the wheel if possible.
Note that slight changes in frequency in Hz and perceived pitch may not be the same thing. Perceived pitch resolution seems to vary with absolute frequency, duration, and loudness. If you want more accuracy than this, there might be some research papers on estimating the time between each glottal closure (probably using a deconvolution or pattern matching technique), which would give you some sort of pitch period. The simplest pitch estimate might be some form of weighted autocorrelation, for which lots of canned algorithms and code is available.
Since dB is log scale, this measure might be somewhat closer to perceived loudness, but has to be spectrally weighted with some perceptual frequency response curve over some duration of measurement.
There seem to be research papers on both of these topics, as well as many textbooks on human audio perception as well as on common audio DSP techniques.
I suggest you read this article
http://audition.ens.fr/adc/pdf/2002_JASA_YIN.pdf
. This is one of the simplest methods of pitch detection, and it works very well.
Also, for measuring the instantaneous power of the signal, you can just take the absolute value of the signal and divide by 1/√2 (Gives the RMS value) and then smooth it (usually a first order low pass filter). I hope this helps. Good luck!
I'm trying to do real time pitch detection of a users singing, but I'm running into alot of problems. I've tried lots of methods, including FFT (FFT Problem (Returns random results)) and autocorrelation (Autocorrelation pitch detection returns random results with mic input), but I can't seem to get any methods to give a good result. Can anyone suggest a method for real-time pitch tracking or how to improve on a method I already have? I can't seem to find any good C / C++ methods for real time pitch detection.
Thanks,
Niall.
Edit: Just to note, i've checked that the mic input data is correct, and that when using a sine wave the results are more or less the correct pitch.
Edit: Sorry this is late, but at the moment, im visualizing the autocolleration by taking the values out of the results array, and each index, and plotting the index on the X axis and the value on the Y axis (both are divided by 100000 or something, and im using OpenGL), plugging the data into a VST host and using VST plugins isn't an option to me. At the moment, it just looks like some random dots. Am i doing it correctly, or can you please point me torwards some code for doing it or help me understand how to visualize the raw audio data and autocorrelation data.
Taking a step back... To get this working you MUST figure out a way to plot intermediate steps of this process. What you're trying to do is not particularly hard, but it is error prone and fiddly. Clipping, windowing, bad wiring, aliasing, DC offsets, reading the wrong channels, the weird FFT frequency axis, impedance mismatches, frame size errors... who knows. But if you can plot the raw data, and then plot the FFT, all will become clear.
I found several open source implementations of real-time pitch tracking
dywapitchtrack uses a wavelet-based algorithm
"Realtime C# Pitch Tracker" uses a modified autocorrelation approach now removed from Codeplex - try searching on GitHub
aubio (mentioned by piem; several algorithms are available)
There are also some pitch trackers out there which might not be designed for real-time, but may be usable that way for all I know, and could also be useful as a reference to compare your real-time tracker to:
Praat is an open source package sometimes used for pitch extraction by linguists and you can find the algorithm documented at http://www.fon.hum.uva.nl/paul/praat.html
Snack and WaveSurfer also contain a pitch extractor
I know this answer isn't going to make everyone happy but here goes.
This stuff is hard, very hard. Firstly go read as many tutorials as you can find on FFT, Autocorrelation, Wavelets. Although I'm still struggling with DSP I did get some insights from the following.
https://www.coursera.org/course/audio the course isn't running at the moment but the videos are still available.
http://miracle.otago.ac.nz/tartini/papers/Philip_McLeod_PhD.pdf thesis about the development of a pitch recognition algorithm.
http://dsp.stackexchange.com a whole site dedicated to digital signal processing.
If like me you didn't do enough maths to completely follow the tutorials don't give up as some of the diagrams and examples still helped me to understand what was going on.
Next is test data and testing. Write yourself a library that generates test files to use in checking your algorithm/s.
1) A super simple pure sine wave generator. So say you are looking at writing YAT(Yet Another Tuner) then use your sine generator to create a series of files around 440Hz say from 420-460Hz in varying increments and see how sensitive and accurate your code is. Can it resolve to within 5Hz, 1Hz, finer still?
2) Then upgrade your sine wave generator so that it adds a series of weaker harmonics to the signal.
3) Next are real world variations on harmonics. So whilst for most stringed instruments you'll see a series of harmonics as simple multiples of the fundamental frequency F0, for instruments like clarinets and flutes because of the way the air behaves in the chamber the even harmonics will be missing or very weak. And for some instruments F0 is missing but can be determined from the distribution of the other harmonics. F0 being what the human ear perceives as pitch.
4) Throw in some deliberate distortion by shifting the harmonic peak frequencies up and down in an irregular manner
The point being that if you are creating files with known results then its easier to verify that what you are building actually works, bugs aside of course.
There are also a number of "libraries" out there containing sound samples.
https://freesound.org from the Coursera series mentioned above.
http://theremin.music.uiowa.edu/MIS.html
Next be aware that your microphone is not perfect and unless you have spent thousands of dollars on it will have a fairly variable frequency response range. In particular if you are working with low notes then cheaper microphones, read the inbuilt ones in your PC or Phone, have significant rolloff starting at around 80-100Hz. For reasonably good external ones you might get down to 30-40Hz. Go find the data on your microphone.
You can also check what happens by playing the tone through speakers and then recording with you favourite microphone. But of course now we are talking about 2 sets of frequency response curves.
When it comes to performance there are a number of freely available libraries out there although do be aware of the various licensing models.
Above all don't give up after your first couple of tries. Best of luck.
Here's the C++ source code for an unusual two-stage algorithm that I devised which can do Realtime Pitch Detection on polyphonic MP3 files while being played on Windows. This free application (PitchScope Player, available on web) is frequently used to detect the notes of a guitar or saxophone solo upon a MP3 recording. The algorithm is designed to detect the most dominant pitch (a musical note) at any given moment in time within a MP3 music file. Note onsets are accurately inferred by a significant change in the most dominant pitch (a musical note) at any given moment during the MP3 recording.
When a single key is pressed upon a piano, what we hear is not just one frequency of sound vibration, but a composite of multiple sound vibrations occurring at different mathematically related frequencies. The elements of this composite of vibrations at differing frequencies are referred to as harmonics or partials. For instance, if we press the Middle C key on the piano, the individual frequencies of the composite's harmonics will start at 261.6 Hz as the fundamental frequency, 523 Hz would be the 2nd Harmonic, 785 Hz would be the 3rd Harmonic, 1046 Hz would be the 4th Harmonic, etc. The later harmonics are integer multiples of the fundamental frequency, 261.6 Hz ( ex: 2 x 261.6 = 523, 3 x 261.6 = 785, 4 x 261.6 = 1046 ). Linked at the bottom, is a snapshot of the actual harmonics which occur during a polyphonic MP3 recording of a guitar solo.
Instead of a FFT, I use a modified DFT transform, with logarithmic frequency spacing, to first detect these possible harmonics by looking for frequencies with peak levels (see diagram below). Because of the way that I gather data for my modified Log DFT, I do NOT have to apply a Windowing Function to the signal, nor do add and overlap. And I have created the DFT so its frequency channels are logarithmically located in order to directly align with the frequencies where harmonics are created by the notes on a guitar, saxophone, etc.
Now being retired, I have decided to release the source code for my pitch detection engine within a free demonstration app called PitchScope Player. PitchScope Player is available on the web, and you could download the executable for Windows to see my algorithm at work on a mp3 file of your choosing. The below link to GitHub.com will lead you to my full source code where you can view how I detect the harmonics with a custom Logarithmic DFT transform, and then look for partials (harmonics) whose frequencies satisfy the correct integer relationship which defines a 'pitch'.
My Pitch Detection Algorithm is actually a two-stage process: a) First the ScalePitch is detected ('ScalePitch' has 12 possible pitch values: {E, F, F#, G, G#, A, A#, B, C, C#, D, D#} ) b) and after ScalePitch is determined, then the Octave is calculated by examining all the harmonics for the 4 possible Octave-Candidate notes. The algorithm is designed to detect the most dominant pitch (a musical note) at any given moment in time within a polyphonic MP3 file. That usually corresponds to the notes of an instrumental solo. Those interested in the C++ source code for my Two-Stage Pitch Detection algorithm might want to start at the Estimate_ScalePitch() function within the SPitchCalc.cpp file at GitHub.com.
https://github.com/CreativeDetectors/PitchScope_Player
Below is the image of a Logarithmic DFT (created by my C++ software) for 3 seconds of a guitar solo on a polyphonic mp3 recording. It shows how the harmonics appear for individual notes on a guitar, while playing a solo. For each note on this Logarithmic DFT we can see its multiple harmonics extending vertically, because each harmonic will have the same time-width. After the Octave of the note is determined, then we know the frequency of the Fundamental.
I had a similar problem with microphone input on a project I did a few years back - turned out to be due to a DC offset.
Make sure you remove any bias before attempting FFT or whatever other method you are using.
It is also possible that you are running into headroom or clipping problems.
Graphs are the best way to diagnose most problems with audio.
Take a look at this sample application:
http://www.codeproject.com/KB/audio-video/SoundCatcher.aspx
I realize the app is in C# and you need C++, and I realize this is .Net/Windows and you're on a mac... But I figured his FFT implementation might be a starting reference point. Try to compare your FFT implementation to his. (His is the iterative, breadth-first version of Cooley-Tukey's FFT). Are they similar?
Also, the "random" behavior you're describing might be because you're grabbing data returned by your sound card directly without assembling the values from the byte-array properly. Did you ask your sound card to sample 16 bit values, and then gave it a byte-array to store the values in? If so, remember that two consecutive bytes in the returned array make up one 16-bit audio sample.
Java code for a real-time real detector is available at http://code.google.com/p/freqazoid/.
It works fairly well on any computer running post-2008 real-time Java. The project has been dropped and could be picked up by any interested party. Contact me if you want further details.
Check out aubio, and open source library which includes several state-of-the-art methods for pitch tracking.
I have asked a similar question here:
C/C++/Obj-C Real-time algorithm to ascertain Note (not Pitch) from Vocal Input
EDIT:
Performous contains a C++ module for realtime pitch detection
Also Yin Pitch-Tracking algorithm
You could do real time pitch detection, be it of a singer's voice, with TarsosDSP
https://github.com/JorenSix/TarsosDSP
just in case anyone hasn't heard of it yet :-)
Can you adapt anything from instrument tuners? My delightfully compact guitar tuner is able to detect the pitch of the strings pretty well. I see this reference to a piano tuner which explains an algorithm to some extent.
Here are some open source libraries that implement pitch detection:
WORLD : speech analysis/synthesis toolkit. This is especially suitable if your source signal is voice.
aubio : audio feature extraction library. Implements many pitch detection algorithms.
Pitch detection : a collection of pitch detection algorithms implemented in C++.
dywapitchtrack : a high quality pitch detection algorithm.
YIN : another implementation of the YIN algorithm in a single C++ source file.
I have a bunch of different audio recordings in WAV format (all different instruments and pitches), and I want to "normalize" them so that they all sound approximately the same volume when played.
I've tried measuring the average sample magnitude (the sum of all absolute values divided by the number of samples), but normalizing by this measurement doesn't work very well. I think this method isn't working because it doesn't take into account the frequency of the sounds, and I know that higher-frequency recordings sound louder than lower-frequency sounds of the same amplitude.
Does anyone know a good method for measuring the loudness of a sound?
Root Mean Square is often used to estimate the loudness of sound files. This is because a sound that is very loud might not be perceived that way if it is very short. Also remember that power increases exponentially with the square of amplitude.
The audio geeks at Hydrogen Audio know a ton about this stuff...check out their free Replay Gain software. You may not need to do any programming at all.
EDIT: Included comment feedback on power vs. amplitude.
To add to PeterAllenWebb's response:
Before you calculate the RMS, you should "center" your sample first (think of a 5-minute .wav where each sample has the maximum +amplitude). The best way to do that is to use a highpass filter at a subsonic frequency.
That would still not take the frequencies that humans are sensitive to in count. To do that, you could use A-weighting. There's a page where you can calculate it online:
http://www.diracdelta.co.uk/science/source/a/w/aweighting/source.html
The code seems to be here:
http://www.diracdelta.co.uk/science/source/a/w/aweighting/multicalc.js
Well not being an expert on audio and adding to the previous comment, you should figure out what you define as the "shortest amount of time for peak power" and then just convert the wave to raw floating point and use RMS over the stretch of time and continuously take chunks of that length of time, find the MAX and there you have your highest peak power.
To reiterate what some other people have said, use RMS value to estimate the "loudness" of a passage of sound.
But, if you're dealing with impulsive sounds like plucking or drum hits, you'd want to do a sliding RMS value and pick out only the peak RMS value. Measure 100 ms of the sound, slide the window, measure again, etc. and then normalize according to the largest value you find.
Definitely remove any DC value before doing the RMS, and A-weighting will make it more like how we hear. Here's code for A-weighting in MATLAB/Octave and Python.
I might be way off here, but, if you have wavepad you can load in multiple files and mess with the volumes a little bit so they are all the same. Also, if you have certain sections of a file that are louder, you can select that section and lower the volume for that one section.
EDIT: And sorry, it;s not really a "method" for measuring volume, but if you just need to make them all the same this should work fine.