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There's this tech-festival in IIT-Bombay, India, where they're having an event called "Artbots" where we're supposed to design artbots with artistic abilities. I had an idea about a musical robot which takes a song as input, detects the notes in the song and plays it back on a piano. I need some method which will help me compute the pitches of the notes of the song. Any idea/suggestion on how to go about it?
This is exactly what I'm doing here as my last year project :) except one thing that my project is about tracking the pitch of human singing voice (and I don't have the robot to play the tune)
The quickest way I can think of is to utilize BASS library. It contains ready-to-use function that can give you FFT data from default recording device. Take a look at "livespec" code example that comes with BASS.
By the way, raw FFT data will not enough to determine fundamental frequency. You need algorithm such as Harmonic Product Spectrum to get the F0.
Another consideration is the audio source. If you are going to do FFT and apply Harmonic Product Spectrum on it. You will need to make sure the input has only one audio source. If it contains multiple sources such as in modern songs there will be to many frequencies to consider.
Harmonic Product Spectrum Theory
If the input signal is a musical note,
then its spectrum should consist of a
series of peaks, corresponding to
fundamental frequency with harmonic
components at integer multiples of the
fundamental frequency. Hence when we
compress the spectrum a number of
times (downsampling), and compare it
with the original spectrum, we can see
that the strongest harmonic peaks line
up. The first peak in the original
spectrum coincides with the second
peak in the spectrum compressed by a
factor of two, which coincides with
the third peak in the spectrum
compressed by a factor of three.
Hence, when the various spectrums are
multiplied together, the result will
form clear peak at the fundamental
frequency.
Method
First, we divide the input signal into
segments by applying a Hanning window,
where the window size and hop size are
given as an input. For each window,
we utilize the Short-Time Fourier
Transform to convert the input signal
from the time domain to the frequency
domain. Once the input is in the
frequency domain, we apply the
Harmonic Product Spectrum technique to
each window.
The HPS involves two steps:
downsampling and multiplication. To
downsample, we compressed the spectrum
twice in each window by resampling:
the first time, we compress the
original spectrum by two and the
second time, by three. Once this is
completed, we multiply the three
spectra together and find the
frequency that corresponds to the peak
(maximum value). This particular
frequency represents the fundamental
frequency of that particular window.
Limitations of the HPS method
Some nice features of this method
include: it is computationally
inexpensive, reasonably resistant to
additive and multiplicative noise, and
adjustable to different kind of
inputs. For instance, we could change
the number of compressed spectra to
use, and we could replace the spectral
multiplication with a spectral
addition. However, since human pitch
perception is basically logarithmic,
this means that low pitches may be
tracked less accurately than high
pitches.
Another severe shortfall of the HPS
method is that it its resolution is
only as good as the length of the FFT
used to calculate the spectrum. If we
perform a short and fast FFT, we are
limited in the number of discrete
frequencies we can consider. In order
to gain a higher resolution in our
output (and therefore see less
graininess in our pitch output), we
need to take a longer FFT which
requires more time.
from: http://cnx.org/content/m11714/latest/
Just a comment: The fundamental harmonic may as well be missing from a (harmonic) sound, this doesn't change the perceived pitch. As a limit case, if you take a square wave (say, a C# note) and completely suppress the first harmonic, the perceived note is still C#, in the same octave. In a way, our brain is able to compensate the absence of some harmonics, even the first, when it guesses a note.
Hence, to detect a pitch with frequency-domain techniques you should take into account all the harmonics (local maxima in the magnitude of the Fourier transform), and extract some sort of "greatest common divisor" of their frequencies. Pitch detection is not a trivial problem at all...
DAFX has about 30 pages dedicated to pitch detection, with examples and Matlab code.
Autocorrelation - http://en.wikipedia.org/wiki/Autocorrelation
Zero-crossing - http://en.wikipedia.org/wiki/Zero_crossing (this method is used in cheap guitar tuners)
Try YAAPT pitch tracking, which detects fundamental frequency in both time and frequency domains. You can download Matlab source code from the link and look for peaks in the FFT output using the spectral process part.
Python package http://bjbschmitt.github.io/AMFM_decompy/pYAAPT.html#
Did you try Wikipedia's article on pitch detection? It contains a few references that can be interesting to you.
In addition, here's a list of DSP applications and libraries, where you can poke around. The list only mentions Linux software packages, but many of them are cross-platform, and there's a lot of source code you can look at.
Just FYI, detecting the pitch of the notes in a monophonic recording is within reach of most DSP-savvy people. Detecting the pitches of all notes, including chords and stuff, is a lot harder.
Just a thought - but do you need to process a digital audio stream as input?
If not, consider using a symbolic representation of music (such as MIDI). The pitches of the notes will then be stated explicitly, and you can synthesize sounds (and movements) corresponding to the pitch, rhythm and many other musical parameters extremely easily.
If you need to analyse a digital audio stream (mp3, wav, live input, etc) bear in mind that while pitch detection of simple monophonic sounds is quite advanced, polyphonic pitch detection is an unsolved problem. In this case, you may find my answer to this question helpful.
For extracting the fundamental frequency of the melody from polyphonic music you could try the MELODIA plug-in: http://mtg.upf.edu/technologies/melodia
Extracting the F0's of all the instruments in a song (multi-F0 tracking) or transcribing them into notes is an even harder task. Both melody extraction and music transcription are still open research problems, so regardless of the algorithm/tool you use don't expect to obtain perfect results for either.
If you're trying to detect the notes of a polyphonic recording (multiple notes at the same time) good luck. That's a very tricky problem. I don't know of any way to listen to, say, a recording of a string quartet and have an algorithm separate the four voices. (Wavelets maybe?) If it's just one note at a time, there are several pitch tracking algorithms out there, many of them mentioned in other comments.
The algorithm you want to use will depend on the type of music you are listening to. If you want it to pick up people singing there are a lot of good algorithms out there designed specifically for voice. (That's where most of the research is.) If you are trying to pick up specific instruments you'll have to be a bit more creative. Voice algorithms can be simple because the range of the human singing voice is generally limited to about 100-2000 Hz. (Speaking range is much more narrow). The fundamental frequencies on a piano, however, go from about 27 Hz. to 4200 Hz., so you're dealing with a wider range usually ignored by voice pitch detection algorithms.
The waveform of most instruments is going to be fairly complex, with lots of harmonics, so a simple approach like counting zeros or just taking the autocorrelation won't work. If you knew roughly what frequency range you were looking in you could low-pass filter and then zero count. I'd think you'd be better off though with a more complex algorithm such as the Harmonic Product Spectrum mentioned by another user, or YAAPT ("Yet Another Algorithm for Pitch Tracking"), or something similar.
One last problem: some instruments, the piano in particular, will have the problem of missing fundamentals and inharmonicity. Missing fundamentals can be dealt with by the pitch tracking algorithms...in fact they have to be since fundamentals are often cut out in electronic transmission...though you'll probably still get some octave errors. Inharmonicity however, will give you problems if somebody plays a note in the bottom octaves of the piano. Normal pitch tracking algorithms aren't designed to deal with inharmonicity because the human voice is not significantly inharmonic.
You basically need a spectrum analyzer. You might be able to to a FFT on a recording of an analog input, but much depends on the resolution of the recording.
what immediately comes to my mind:
filter out very low frequencies (drums, bass-line),
filter out high frequencies (harmonics)
FFT,
look for peaks in the FFT output for the melody
I am not sure, if that works for very polyphonic sounds - maybe googling for "FFT, analysis, melody etc." will return more info on possible problems.
regards
Related
My friend Prasad Raghavendra and me, were trying to experiment with Machine Learning on audio.
We were doing it to learn and to explore interesting possibilities at any upcoming get-togethers.
I decided to see how deep learning or any machine learning can be fed with certain audios rated by humans (evaluation).
To our dismay, we found that the problem had to be split to accommodate for the dimensionality of input.
So, we decided to discard vocals and assess by accompaniments with an assumption that vocals and instruments are always correlated.
We tried to look for mp3/wav to MIDI converter. Unfortunately, they were only for single instruments on SourceForge and Github and other options are paid options. (Ableton Live, Fruity Loops etc.) We decided to take this as a sub-problem.
We thought of FFT, band-pass filters and moving window to accommodate for these.
But, we are not understanding as to how we can go about splitting instruments if chords are played and there are 5-6 instruments in file.
What are the algorithms that I can look for?
My friend knows to play Keyboard. So, I will be able to get MIDI data. But, are there any data-sets meant for this?
How many instruments can these algorithms detect?
How do we split the audio? We do not have multiple audios or the mixing matrix
We were also thinking about finding out the patterns of accompaniments and using those accompaniments in real-time while singing along. I guess we will be able to think about it once we get answers to 1,2,3 and 4. (We are thinking about both Chord progressions and Markovian dynamics)
Thanks for all help!
P.S.: We also tried FFT and we are able to see some harmonics. Is it due to Sinc() in fft when rectangular wave is input in time domain? Can that be used to determine timbre?
We were able to formulate the problem roughly. But, still, we are finding it difficult to formulate the problem. If we use frequency domain for certain frequency, then the instruments are indistinguishable. A trombone playing at 440 Hz or a Guitar playing at 440 Hz would have same frequency excepting timbre. We still do not know how we can determine timbre. We decided to go by time domain by considering notes. If a note exceeds a certain octave, we would use that as a separate dimension +1 for next octave, 0 for current octave and -1 for the previous octave.
If notes are represented by letters such as 'A', 'B', 'C' etc, then the problem reduces to mixing matrices.
O = MI during training.
M is the mixing matrix that will have to be found out using the known O output and I input of MIDI file.
During prediction though, M must be replaced by a probability matrix P which would be generated using previous M matrices.
The problem reduces to Ipredicted = P-1O. The error would then be reduced to LMSE of I. We can use DNN to adjust P using back-propagation.
But, in this approach, we assume that the notes 'A','B','C' etc are known. How do we detect them instantaneously or in small duration like 0.1 seconds? Because, template matching may not work due to harmonics. Any suggestions would be much appreciated.
Splitting out the different parts is a machine learning problem all to its own. Unfortunately, you can't look at this problem in audio land only. You must consider the music.
You need to train something to understand musical patterns and progressions in the context of the type of music you give it. It needs to understand what the different instruments sound like, both mixed and not mixed. It needs to understand how these instruments are often played together, if it's going to have any chance at all at separating what's going on.
This is a very, very difficult problem.
This is a very hard problem mainly because converting audio to pitch isnt very simple due to Nyquist folding harmonics that are 22Khz+ back down and also other harmonic introductions such as saturators/distortion and other analogue equipment that introduce harmonics.
The fundamental harmonic isnt always the loudest which is why your plan will not work.
The hardest thing to measure would be a distorted guitar. The harmonic some pedals/plugins can make is crazy.
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I have been searching for emotion detection through voice/speech solution on mobile (iOS) and web.
I found Moodies-iOS and Vokaturi solution, but they are not free.
I couldn't find any open source or paid version software available to integrate in my app and test the solution.
Could someone share if you have any info on this related.
Is there any OPEN SOURCE for iOS for Emotion analysis and detection through Voice/Speech, Please let me know.
As a former research in affective computing, I highly doubt you can find a ready-for-use iOS open source solution for emotion recognition from speech. The main reason is that it is a damn difficult task that requires a lot of research and a lot of proper data to train models. That is why companies like BeyondVerbal and Vokaturi do not share their models with others. Thus, you will be very lucky if you can find anything in open source, I am not even talking about iOS solutions.
I am aware about some toolkits you can use for this task (namely, the openEAR toolkit), but to build something working from it, you need an expert knowledge in the field and data to train models. A comprehensive list of databases can be found here: http://emotion-research.net/wiki/Databases. A lot of them a freely available.
As Dmytro Prylipko said it is very doubtful that there is any open-source lib for emotion recognition from speech.
You may write your own solution. It is not hard. Trouble is, as mentioned before, proper training and/or trasholding takes a lot of time and nerves.
I will give you a short theory how you should begin writing the algo, but training and so on is on you.
First big trouble is that different people differently relay their emotions vocally.
For example: one shocked person will to their shock respond with overexclaimed sentence while another will "freeze" and their response would sound very flat (almost robot-like).
Therefore you will need a lot of templates from which to learn how to classify your input speech by emotions.
You can remove some difficulties by using context recognition along with voice prosody.
That is what I'd advise you to do.
First make an algorithm that will use speech-recognized text to put it into emotion context. E.g. you can use specific words and phrases that people use when expressing different emotions.
That is easily done. You may use a neural network or simple branching or whatever.
So you will be able to recognize whether person is thankful and surprised at the same time by combining context recognition and emotions from prosody.
Now, to recognize the emotion from prosody you have to get prosody parameters and some others.
For example, some emotions may be recognized by looking at duration of particular words in a sentence.
So you have the sentence and the text of that sentence. You know that the speed of normal speech is approximately 200 words per minute. Knowing this and number of words in the sentence you can see how fast is someone talking. Then you measure the duration of each word and get its speed. By knowing how fast is the speech and how long is the word you can get normalized ratios that can be used for classifications in order to determine the closest guess of the emotion.
For instance, when someone is presented with a present that he/she likes very much, the "thank you" will sound pretty long. It will also be of higher pitch than that person's usual speech.
So the next step would be to get the average pitch for each word to see the relation between them. So you will be able to see how the sentence prosody modulates. From lower to higher, or vice versa.
Also, how prosody changes inside the phrases within the sentence.
You may go about this by comparing curves of known emotion directly, or you may use aproximation to get coefficients from the prosody curve vector. The square function does good for normal speech prosody (with no particular emotions in). So some higher order polynomial should do. So, you can get coefficients of the polynom and use them to get what emotion should whole sentence or phrase relay.
The same goes for individual words within the sentence. You get the pitch for each phoneme or syllable or just the pitch curve for e.g. every 20 ms of the word. Then you either calculate few coefficients to aproximate the polynom you decided is good enough for you, or you take the whole curve and normalize it to e.g. 30 points to use it with recognition.
To compare curves directly you may use gesture recognition algorithm by Oleg Dopertchouk:
http://www.gamedev.net/reference/articles/article2039.asp
I tried it on pitch curves of melodies, it works just fine.
The trouble is, you need a database of speech with context and emotion with clear manually done classification to give your algo something to compare with.
If you use polynomials instead of whole curves, you can do some recognition by using thresholds on coefficients, but results will be a bit shaky. Only real excuse for using coeffs at all is that you do not need to know how long is the word in question. I.e. the same polynom should work on a word with 2 phonemes and on one with 5. (should work)
You see, a theory is nice and easy. Use speech recognition, measure speech rate, and duration of each word, construct pitch curve for whole phrase and pitch curve for each word using FFT, do some comparison between ready database and the input. And walla, emotion recognized.
But where will you find the database with word curves marked with emotions.
For example, you would need for each emotion at least one pitch curve for words with different number of phonemes. At least one, because it is important whether the word starts with vowel or ends with one, or simply someone differently relays the same emotion even if the curve represents the same word.
OK, so you can say that you can make one. Where would you find recorded samples to make your curves or calculate coeffs? Hm, perhaps a recording of some drama. Not bad idea, but the acted emotions aren't the same as the natural ones.
It is a big job to teach a machine such a thing.
Oh, yeah, I almost forgot, emotions aren't only, or sometimes at all transfered using pitch changes, sometimes it's only the way in which the word is being pronounced.
So, for some cases, you would probably need LPC or some other coefficients showing some more info on how phonemes in the word sounds. Or you would need to take in view other harmonics from FFT, not just the one representing the pitch of excitation train.
The best that you can do without following my hints and developing your own algo, is to use NLTK (natural language toolkit) to develop a statistical speech (emotionally rich) model and use algorithms from there (perhaps a bit modified) to try to get to the emotion in question.
But I fear it would be a greater job than going from zero. As far as I know NLTK doesn't support emotions. Just normal speech prosody.
You may try to integrate some things I wrote about into Sphinx, to develop emotion based speech models and introduce emotion recognition directly into sphinxes VR algorithm.
If you really need this, I advise you to learn enough DSP to write your own algo, then pay someone to make you initial database from audiobooks, radio dramas and similar stuff (using a tool you provide).
After your algo starts to work reasonably well, implement autolearning by giving users an option to correct the algo's wrong guesses. After some time you will get 90% reliable algo to recognize emotions from speech.
This is for a personal project of mine, and I have no idea from where to start as it falls way beyond my comfort zone.
I know that there are a few language learning software out there that allows the user to record his or her voice and compare the pronounce with a native speaker of said language.
My question is, how to achieve this?
I mean, how one compares the pronunciation between the user and the native speaker?
If you're looking for something relatively simple, you could simply compute the MFCC (http://en.wikipedia.org/wiki/Mel-frequency_cepstrum) of the recording, and then look at something simple like the correlation between the recording and the average coefficients of that word being pronounced by a native speaker. The MFCC will transform the audio into a space where euclidean distance corresponds more closely with perceptual difference.
Of course, there are several possible problems:
Aligning the two recordings so the coefficients match up. To fix this, you could look at the maximum cross-correlation of the coefficients, rather than the simple correlation, so you will get an automatic "best alignment" for free. Also, you might have to clip off ends of the recording, so only the actual pronunciation of the word remains in the recording.
The MFCC maps to perceptual space, but might not correspond so well to accent inaccuracies. You could perhaps try to fix this by instead of comparing it to just the "ideal" pronunciation, comparing it to the average for several different types of mispronunciation, and looking at which model it is closest to.
Even good accented words will be on average some "distance" from the ideal. You'll have to take that into account, and compare the input's distance to the "relative" good distance.
Correlation might not be the best way to compare the relative similarity of two sounds. Experiment with lots of different metrics... try different L^p norms: (http://en.wikipedia.org/wiki/Lp_space), or try weighing the different MFCCs differently (if I recall, even after MFCC have been taken, although they are all supposed to have the same perceptual "weight", the ones in the middle are still more important for how we perceive a sound than the high or low ones.)
There might be certain parts of the sound where the pronunciation matters much more for the quality of the accent. Perhaps transient detection to find those positions and mark them as more important would be good. If you had a whole bunch of "good pronunciation" and "bad pronunciation" examples, you could probably automatically extract those locations.
Again, in the end the only way you're going to know which combination of these options works best is by testing.
I've read about adapting gaussian mixture models for the phonetic space of a general speaker to an individual. This might be useful for training for a non-canonical accent for private use.
If you just compare the speaker to a general pronunciation model, then the match might not be very good. So the idea is to adjust the models to fit the speaker better during individual training.
Speaker Verification using Adapted Gaussian Mixture Models
EDIT: looking over your question again, I think I answered a different question. But the technique uses similar models:
Model various language (Do you have lots of data for different languages? Collecting the data might be the hard part.) GMMs work well for this.
Compare the data point from the speaker to the various language models
Choose the model that is the best predictor for the speaker data as the winner.
I plan to write a conversation analysis software, which will recognize the individual speakers, their pitch and intensity. Pitch and intensity are somewhat straightforward (pitch via autocorrelation).
How would I go about recognizing individual speakers, so I can record his/her features? Will storing some heuristics for each speaker's frequencies be enough? I can assume that only one person speaks at a time (strictly non-overlapping). I can also assume that for training, each speaker can record a minute's worth of data before actual analysis.
Pitch and intensity on their own tell you nothing. You really need to analyse how pitch varies. In order to identify different speakers you need to transform the speech audio into some kind of feature space, and then make comparisons against your database of speakers in this feature space. The general term that you might want to Google for is prosody - see e.g. http://en.wikipedia.org/wiki/Prosody_(linguistics). While you're Googling you might also want to read up on speaker identification aka speaker recognition, see e.g. http://en.wikipedia.org/wiki/Speaker_identification
If you are still working on this... are you using speech-recognition on the sound input? Because Microsoft SAPI for example provides the application with a rich API for digging into the speech sound wave, which could make the speaker-recognition problem more tractable. I think you can get phoneme positions within the waveform. That would let you do power-spectrum analysis of vowels, for example, which could be used to generate features to distinguish speakers. (Before anybody starts muttering about pitch and volume, keep in mind that the formant curves come from vocal-tract shape and are fairly independent of pitch, which is vocal-cord frequency, and the relative position and relative amplitude of formants are (relatively!) independent of overall volume.) Phoneme duration in-context might also be a useful feature. Energy distribution during 'n' sounds could provide a 'nasality' feature. And so on. Just a thought. I expect to be working in this area myself.
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I am trying to use ANN for pitch detection of musical notes. The network is a simple two-layer MLP, whose inputs are basically a DFT (averaged and logarithmically distributed), and 12 outputs correspond to the 12 notes of a particular octave.
The network is trained with several samples of those 12 notes played by some instrument (one note at a time), and a few samples of "silence".
The results are actually good. The network is able to detect those notes played by different instruments preety accurately, it's relatively amune to noise, and even doesn't loose it's sanety completely when being played a song.
The goal, however, is to be able to detect polyphonic sound. So that when two or more notes are played together, the two corresponding neurons will fire. The surprising thing is that the network actually already does that to some extent (being trained over monophonic samples only), however less consistently and less accurately than for monophonic notes. My question is how can I enhance it's ability to recognise polyphnic sound?
The problem is I don't truely understand why it actually works already. The different notes (or their DFTs) are basically different points in space for which the network is trained. So I see why it does recognise similiar sounds (nearby points), but not how it "concludes" the output for a combination of notes (which form a distant point from each of the training examples). The same way an AND network which is trained over (0,0) (0,1) (1,0) = (0), is not expected to "conclude" that (1,1) = (1).
The brute force aprroach to this is to train the network with as many polyphonic samples as possible. However, since the network seem to somehow vaguely grasp the idea from the monophonic samples, there's probably something more fundemential here.
Any pointers? (sorry for the length, btw :).
The reason it works already is probably quite simply that you didn't train it to pick one and only one output (at least I assume you didn't). In the simple case when the output is just a dot product of the input and the weights, the weights would become matched filters for the corresponding pitch. Since everything is linear, multiple outputs would simultaneously get activated if multiple matched filters simultaneously saw good matches (as is the case for polyphonic notes). Since your network probably includes nonlinearities, the picture is a bit more complex, but the idea is probably the same.
Regarding ways to improve it, training with polyphonic samples is certainly one possibility. Another possibility is to switch to a linear filter. The DFT of a polyphonic sound is basically the sum of DFTs of each individual sound. You want a linear combination of inputs to become a corresponding linear combination of outputs, so a linear filter is appropriate.
Incidentally, why do you use a neural network for this in the first place? It seems that just looking at the DFT and, say, taking the maximum frequency would give you better results more easily.
Anssi Klapuri is a well-respected audio researcher who has published a method to perform pitch detection upon polyphonic recordings using Neural Networks.
You might want to compare Klapuri's method to yours. It is fully described in his master's thesis, Signal Processing Methods for the Automatic Transcription of Music. You can find his many papers online, or buy his book which explains his algorithm and test results. His master's thesis is linked below.
https://www.cs.tut.fi/sgn/arg/klap/phd/klap_phd.pdf
Pitch Detection upon polyphonic recordings is a very difficult topic and contains many controversies -- be prepared to do a lot of reading. The link below contains another approach to pitch detection upon polyphonic recordings which I developed for a free app called PitchScope Player. My C++ source code is available on GitHub.com, and is referenced within the link below. A free executable version of PitchScope Player is also available on the web and runs on Windows.
Real time pitch detection
I experimented with evolving a CTRNN (Continuous Time Recurrent Neural Network) on detecting the difference between 2 sine waves. I had moderate success, but never had time to follow up with a bank of these neurons (ie in bands similar to the cochlear).
One possible approach would be to employ Genetic Programming (GP), to generate short snippets of code that detects the pitch. This way you would be able to generate a rule for how the pitch detection works, which would hopefully be human readable.