I am making a sound engine where I can play and stop sound. My issue is if a user wants to stop the sound I immediately stop it ie I send 0 as PCM value. This has the consequence of producing a pop / click sound because the PCM value drops from lets say 0.7 to 0 immediately causing a pop/click sound which is very annoying to hear.
Here is a discussion about this.
I am looking for an algorithm or a way to deal with these audio clicks / pops. What is the best practice for dealing audio clicks? Is there a universal way to go about this? I am very new to audio DSP and I could not find a good answer for this.
When you cut off the sound abruptly, you are multiplying it by a step-shaped signal.
When you multiply two signals together, you convolve their frequencies. A step-shape has energy at all frequencies, so the multiplication will spread the energy from the sound over all frequencies, making an audible pop.
Instead, you want to fade the sound out over 30ms or so -- that is still very fast, and will sound like an abrupt stop, but there will be no audible pop.
You should use a curve shaped like 1-t2 to modulate the volume, or something else without significant high-frequency components. That way, when it is convolved with the original sound in the frequency domain, it won't produce any new frequencies.
I need to be able to analyze (search thru) hundreds of WAV files and detect but not remove static noise. As done currently now, I must listen to each conversation and find the characteristic noise/static manually, which takes too much time. Ideally, I would need a program that can read each new WAV file and be able to detect characteristic signatures of the static noise such as periods of bursts of white noise or full audio band, high amplitude noise (like AM radio noise over phone conversation such as a wall of white noise) or bursts of peek high frequency high amplitude (as in crackling on the phone line) in a background of normal voice. I do not need to remove the noise but simply detect it and flag the recording for further troubleshooting. Ideas?
I can listen to the recordings and find the static or crackling but this takes time. I need an automated or batch process that can run on its own and flag the troubled call recordings (WAV files for a phone PBX). These are SIP and analog conversations depending on the leg of the conversation so RTSP/SIP packet analysis might be an option, but the raw WAV file is the simplest. I can use Audacity, but this still requires opening each file and looking at the visual representation of the audio spectrometry and is only a little faster than listening to each call but still cumbersome.
I currently have no code or methods for this task. I simply listen to each call wav file to find the noise.
I need a batch Wav file search that can render wav file recordings that contain the characteristic noise or static or crackling over the recording phone conversation.
Unless you can tell the program how the noise looks like, it's going to be challenging to run any sort of batch processing. I was facing a similar challenge and that prompted me to develop (free and open source) software to help user in audio exploration, analysis and signal separation:
App: https://audioexplorer.online/
Docs: https://tracek.github.io/audio-explorer/
Source code: https://github.com/tracek/audio-explorer
Essentially, it visualises audio as a 2d scatter plot rather than only "linear", as in waveform or spectrogram. When you upload audio the following happens:
Onsets are detected (based on high-frequency content algorithm from aubio) according to the threshold you set. Set it to None if you want all.
Per each audio fragment, calculate audio features based on your selection. There's no universal best set of features, all depends on the application. You might try for starter with e.g. Pitch statistics. Consider setting proper values for bandpass filter and sample length (that's the length of audio fragment we're going to use). Sample length could be in future established dynamically. Check docs for more info.
The result is that for each fragment you have many features, e.g. 6 or 60. That means we have then k-dimensional (where k is number of features) structure, which we then project to 2d space with dimensionality reduction algorithm of your selection. Uniform Manifold Approximation and Projection is a sound choice.
In theory, the resulting embedding should be such that similar sounds (according to features we have selected) are closely together, while different further apart. Your noise should be now separated from your "not noise" and form cluster.
When you hover over the graph, in right-upper corner a set of icons appears. One is lasso selection. Use it to mark points, inspect spectrogram and e.g. download table with features that describe that signal. At that moment you can also reduce the noise (extra button appears) in a similar way to Audacity - it analyses the spectrum and reduces these frequencies with some smoothing.
It does not completely solve your problem right now, but could severely cut the effort. Going through hundreds of wavs could take better part of the day, but you will be done. Want it automated? There's CLI (command-line interface) that I am developing at the same time. In not-too-distant future it should take what you have labelled as noise and signal and then use supervised machine learning to go through everything in batch mode.
Suggestions / feedback? Drop an issue on GitHub.
I have created a really basic FFT visualizer using a Teensy microcontroller, a display panel, and a pair of headphone jacks. I used kosme's FFT library for Arduino: https://github.com/kosme/arduinoFFT
Analog audio flows into the headphone input and to a junction where the microcontroller samples it. That junction is also connected to an audio out jack so that audio can be passed to some speakers.
This is all fine and good, but currently I'm only sampling the left audio channel. Any time music is stereo separated, the visualization cannot account for any sound on the right channel. I want to rectify this but I'm not sure whether I should start with hardware or software.
Is there a circuit I should build to mix the left and right audio channels? I figure I could do something like so:
But I'm pretty sure that my schematic is misguided. I included bias voltage to try and DC couple the audio signal so that it will properly ride over the diodes. Making sure that the output matches the input is important to me though.
Or maybe should this best be approached in software? Should I instead just be sampling both channels separately and then doing some math to combine them?
Combining the stereo channels of one end of the fork without combining the other two is very difficult. Working in software is much easier.
If you take two sets of samples, you've doubled the amount of math that the microcontroller needs to do.
But if you take readings from both pins and divide them by two, you can add them together and have one set of samples which represents the 'mono' signal.
Keep in mind that human ears have an uneven response to sound volumes, so a 'medium' volume reading on both pins, summed and halved, will result in a 'lower-medium' value. It's better to divide by 1.5 or 1.75 if you can spare the cycles for more complicated division.
I am developing app to detect the inability of elderly people to unlock their rooms using IC cards in their daycare center.
This room doors has an electronic circuit that emits beep sounds d to signal the user failure in unlock the room. My goal is to detect this beep signal.
I have searched a lot and found some possibilities:
To clip the beep sound and use as a template signal and compare it with test signal (the complete human door interaction audio clip) using convolution, matched filters, DTW or what so ever to measure their similarity. What do u recommend and how to implement it.
To analyze the FFT of beep sound to see if it has a frequency band different that of the background noise. I do not understand how to do it exactly?
To check whether the beep sound form a peak at certain frequency spectrum that is absent in the background noise. If so, Implement a freclipped the beep sound and got the spectrogram as shown in the figure spectrogram of beep sound. but i cannot interpret it? could u give me a detailed explanation of the spectrogram.
3.What is your recommendation? If you have other efficient method for beep detection, please explain.
There is no need to calculate the full spectrum. If you know the frequency of the beep, you can just do a single point DFT and continuously check the level at that frequency. If you detect a rising and falling edge within a given interval it must be the beep sound.
You might want to have a look at the Goertzel Algorithm. It is an algorithm for continuous single point DFT calculation.
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.