I have no experience in MATLAB and unfortunately my project is in MATLAB.
Basically the objective is to read a music source (preferably in mp3 format but .wav is also OK) into MATLAB and then apply a low pass filter in such a way that it filters everything except the beats. Then it should get the time at which each beat occurs and write the results to a text file.
It's quite a bit easier to work with .wav files I think, although Matlab way well have utilities for such things, in fact it does: Reading .wav
The easiest way to implement a low pass filter is a moving average filter.
The simplest way to do this would be be to loop over the data and take an average of each group of n values. I'm not sure exactly how the cutoff frequency would depend on n, but you could experiment a bit.
Otherwise, I know that there is a signal processing toolkit for Octave and I think that Matlab has a built-in filter function: https://ccrma.stanford.edu/~jos/fp/Matlab_Filter_Implementation.html
A third way which is over the top, would be to perform an FFT and do the filtering in the frequency domain.
Once you have the low-frequency part of the signal you can check for samples that are above an amplitude threshold and output where in the data these were found.
30 seconds on google with the keywords "beat extraction matlab" yield the following two code sources:
Music Audio Tempo Estimation and Beat Tracking
Beat This A Beat Synchronization Project
In Matlab you can use and state of the art Multi Feature beat tracker algorithm, the information of the algorithm is publish here:
J.R. Zapata, M. Davies and E. Gómez, "Multi-feature beat tracker," IEEE/ACM Transactions on Audio, Speech and Language Processing. 22(4), pp. 816-825, 2014. http://dx.doi.org/10.1109/TASLP.2014.2305252
The Matlab implementation of the multifeature beat tracker is:
https://github.com/JoseRZapata/MultiFeatureBeatTracking
Related
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.
Can someone please give me an idea about this problem in matlab ,
I have 4 .wav files that contain the chirping of the birds . Each .wav file represents a different bird. Given an input .wav file , I need to decide which bird it is . I know I have to make frequency spectrum comparison to get to the solution . but don't quite know how i should use spectrogram to help me get there .
P.S. I know how what spectrogram does and have plotted quite a few .wav files with it though
There are several methods for patter recognition problem like the one that you are talking.
You can use a frequency analysis like FFT with the matlab function
S = SPECTROGRAM(X,WINDOW,NOVERLAP)
In SPECTROGRAM you need to define the time window of signal to be analysed in the variable WINDOW. You can use a rectangular window (example WINDOW = [1 1 1 1 1 1 1 ... 1]) with the number of values equal to the desired length. There are a lot of windows to use: hanning, hamming, blackman. You should used the one which is better to your problem. The NOVERLAP is the number of points that your windows moves in one step.
Besides this approach, wavelet transform is also a good technique to solve your problem. Matlab also have a good toolbox to apply discrete and continuous wavelets.
You might try to solve the problem with Deep Belief Networks
Here are some articles that might be helpful:
Audio Feature Extraction with Restricted Boltzmann Machines
Unsupervised feature learning for audio classification
To summarize the idea, instead of manually tune the features, employ RBMs or Autoencoder to extract features (bases) which represent the observed audio samples and then run learning algorithm.
You will need more than 4 audio samples in order to train DBN but it is worth trying as the approach has shown promising results in the past.
This tuturial might be also helpful.
this may prove to be a complicated problem. As a starting point I advise you to divide each record into some fixed length of frames like 20ms with 10ms overlap ,then extract fft of these frames and get some max energy freq. values for each frame. As a last step compare frame frequencies with each other and determine the result by selecting the maximum correlation
So basically, my problem is that I have a speech signal in .wav format that is corrupted by a harmonic noise source at some frequency. My goal is to identify the frequency at which this noise occurs, and use a notch filter to remove said noise. So far, I have read the speech signal into matlab using:
[data, Fs] = wavread('signal.wav');
My question is how can I identify the frequency at which the harmonic noise is occurring, and once I've done that, how can I go about implementing a notch filter at that frequency?
NOTE: I do not have access to the iirnotch() command or fdesign.notch() due to the version of MATLAB I am currently using (2010).
The general procedure would be to analyse the spectrum, to identify the frequency in question, then design a filter around that frequency. For most real applications it's all a bit woolly: the frequencies move around and there's no easy way to distinguish noise from signal, so you have to use clever techniques and a bit of guesswork. However if you know you have a monotonic corruption then, yes, an FFT and a notch filter will probably do the trick.
You can analyse the signal with fft and design a filter with, among others, fir1, which I believe is part of the signal processing toolbox. If you don't have the signal processing toolbox you can do it 'by hand', as in transform to the frequency domain, remove the frequency(ies) you don't want (by zeroing the relevant elements of the frequency vector) and transform back to time domain. There's a tutorial on exactly that here.
The fft and fir1 functions are well documented: search the Mathworks site to get code examples to get you up and running.
To add to/ammend xenoclast's answer, filtering in the frequency domain may or may not work for you. There are many thorny issues with filtering in the frequency domain, some of which are covered here: http://blog.bjornroche.com/2012/08/why-eq-is-done-in-time-domain.html
One additional issue is that if you try to process your entire file at once, the "width" or Q of the filters will depend on the length of your file. This might work out for you, or it might not. If you have many files of different lengths, don't expect similar results this way.
To design your own IIR notch filter, you could use the RBJ audio filter cookbook. If you need help, I wrote up a tutorial here:
http://blog.bjornroche.com/2012/08/basic-audio-eqs.html
My tutorial uses bell/peaking filter, but it's easy to follow that and then replace it with a notch filter from RBJ.
One final note: assuming this is actually an audio signal in your .wav file, you can also use your ears to find and fix the problem frequencies:
Open the file in an audio editing program that lets you adjust filter settings in real-time (I am not sure if audacity lets you do this, but probably).
Use a "boost" or "parametric" filter set to a high gain and sweep the frequency setting until you hear the noise accentuated the most.
replace the boost filter with a notch filter of the same frequency. You may need to tweak the width to trade off noise elimination vs. signal preservation.
repete as needed (due to the many harmonics).
save the resulting file.
Of course, some audio editing apps have built-in harmonic noise reduction features that work especially well for 50/60 Hz noise.
Is it possible to transform speech (pitch/formant shift) in (near) real-time using MATLAB? How can it be done?
If not, what should I use to do that?
I need to get input from the microphone, visualise the sound wave, add a filter to it, see the oscilloscope again, and play back the modified sound.
The real-time visualization (spectrogram) can be created with SparkNG package by Hideki Kawahara.
Sure. There's a demo application up on the MATLAB Central File Exchange that does something similar. It reads in a signal from the sound card (requires Data Acquisition Toolbox) in near real time, applies an FFT transform - you could do something else like applying a filter - and visualises the results in 3D graphs live. You could use it as a template and modify it to your needs, such as visualising in different ways (more of an oscilloscope style), or outputting the sound as a .wav file for later playback.
If you need properly real time, you might look into implementing in Simulink rather than just base MATLAB.
I was looking to implement voice pitch detection in iphone using HPS method. But the detected tones are not very accurate. Performous does a decent job of pitch detection.
I looked through the code but i did not fully get the theory behind the calculations.
They use FFT and find the peaks. But the part where they use the phase of FFT output, got me confused.I figure they use some heuristics for voice frequencies.
So,Could anyone please explain the algorithm used in Performous to detect pitch?
[Performous][1] extracts pitch from the microphone. Also the code is open source. Here is a description of what the algorithm does, from the guy that coded it (Tronic on irc.freenode.net#performous).
PCM input (with buffering)
FFT (1024 samples at a time, remove 200 samples from front of the buffer afterwards)
Reassignment method (against the previous FFT that was 200 samples earlier)
Filtering of peaks (this part could be done much better or even left out)
Combining peaks into sets of harmonics (we call the combination a tone)
Temporal filtering of tones (update the set of tones detected earlier instead of simply using the newly detected ones)
Pick the best vocal tone (frequency limits, weighting, could use the harmonic array also but I don't think we do)
I still wasn't able from this information to figure it out and implement it. If anyone manages this, please please post your results here, and comment this response so that SO notifies me.
The task would be to create a minimal C++ wrapper around this code.