This document is to be distributed for free and without any modification from its original state. The author declines all responsibility in the damage this document or any of the things you will do with it might do to anyone or to anything. This document and any of its contents is not copyrighted and is free of all rights, you may thus use it, modify it or destroy it without breaking any international law. However according to the author's will, you may not use this document for commercial profit directly, but you may use indirectly its intellectual contents; in which case I would be pleased to receive a mail of notice or even thanks. This is my first tutorial and I am still a student, you must assume that this document is probably not free of small errors and bugs. In the same state of mind, those algorithms are not fully optimised, they are explained for pedagogical purposes and you may find some redundant computations or other voluntary clumsiness. Please be indulgent and self criticise everything you might read. Hopefully, lots of this stuff was taken in sources and books of reference; as for the stuff I did: it has proven some true efficiency in test programs I made and which work as wanted. As said in the introduction: If you have any question or any comment about this text, please send it to the above email address, I'll be happy to answer as soon as possible.
Simulating a physical phenomena which obeys to known mathematical equations is, with a number of approximations, always feasable. But what about more abstract concepts, such as feelings, which do not follow any laws? The simplest things we can feel are often the hardest things to capture in a program. Beat detection follows this rule : feeling the beat of a song comes naturally to humans or animals. Indeed it is only a feeling one gets when listening to a melody, a feeling which will make you dance in rhythm or hit a table with your hands on the melody beats. Therefore, how can we teach this beat detection to a machine that can only compute logical operations? In fact there are a number of algorithms which manage to approximate, more or less accurately, this beat detection. We will first study the statistical approach of beat detection on a streaming source and secondly a filtering approach of rhythm extraction on a static song.
This guide assumes the reader has basic signal processing understanding (FFT, convolutions and correlations should sound common) maybe some stuff in statistics will also help (Variance, Average, Principal Components Analysis, will be quoted among others). The point here is not to actually write the code of these algorithms, but more to understand how they work and to be able to adapt or create the appropriate algorithm to a situation. If you have a question or a comment about this text, please send it to the above email address, I'll be happy to answer as soon as possible. Anyway, the aim here is to give more precise ideas on the subject of beat detection to the reader. Enjoy.
I – Statistical streaming beat detection
1 – Simple sound energy
a - A first analysis
The human listening system determines the rhythm of music by detecting a pseudo – periodical succession of beats. The signal which is intercepted by the ear contains a certain energy, this energy is converted into an electrical signal which the brain interprets. Obviously, The more energy the sound transports, the louder the sound will seem. But a sound will be heard as a beat only if his energy is largely superior to the sound's energy history, that is to say if the brain detects a brutal variation in sound energy. Therefore if the ear intercepts a monotonous sound with sometimes big energy peaks it will detect beats, however, if you play a continuous loud sound you will not perceive any beats. Thus, the beats are big variations of sound energy. This first analysis will bring us to our simplest model : Sound energy peaks.
In this model we will detect sound energy variations by computing the average sound energy of the signal and comparing it to the instant sound energy. Lets say we are working in stereo mode with two lists of values : (an) and (bn). (an) contains the list of sound amplitude values captured every Te seconds for the left channel, (bn) the list of sound amplitude values captured every Te seconds for the right channel. So we want to compute the instant energy and the average energy of the signal. The instant energy will in fact be the energy contained in 1024 samples (1024 values of a[n] and b[n]), 1024 samples represent about 5 hundreds of second which is pretty much 'instant'. The average energy should not be computed on the entire song, some songs have both intense passages and more calm parts. The instant energy must be compared to the nearby average energy, for example if a song has an intense ending, the energy contained in this ending shouldn't influence the beat detection at the beginning. We detect a beat only when the energy is superior to a local energy average. Thus we will compute the average energy on say : 44032 samples which is about 1 second, that is to say we will assume that the hearing system only remembers of 1 second of song to detect beat. This 1 second time (44032 samples) is what we could call the human ear energy persistence model; it is a compromise between being to big and taking into account too far away energies, and being too small and becoming to close to the instant energy to make a valuable comparison.
The history buffer where we will keep the last 44032 samples wil contain in fact too lists of samples (B) and (B) corresponding to the left (an) and to the right (bn) channels history.
Simple sound energy algorithm #1:
Every 1024 samples:
b - Some direct optimisations
This was the basic version of the algorithm, its speed and accurecy can be improved quite easily. The algorithm can be optimised by keeping the energy values computed on 1024 samples in history instead of the samples themselves, so that we don't have to compute the average energy on the 44100 samples buffer (B) but on the instant energies history we will call (E). This sound energy history buffer (E) must correspond to approximately 1 second of music, that is to say it must contain the energy history of 44032 samples (calculated on groups of 1024) if the sample rate is 44100 samples per second. Thus E will contain the newest energy computed on the newest 1024 samples, and E will contain the oldest energy computed on the oldest 1024 samples. We have 43 energy values in history, each computed on 1024 samples which makes 44032 samples energy history, which is equivalent to 1 second in real time. The count is good. The value of 1 second represents the persistance of the music energy in the human ear, it was obtain with experimentations but it may varry a little from a person to another, just adjust it as you feal. So here is what the algorithm becomes:
Simple sound energy algorithm #2:
Every 1024 samples:
c - Sensitivity detection
The imediate draw back of this algorithm is the choice of the 'C' constant. For example in techno and rap music beats are quite intense and precise so 'C' should be quite high (about 1.4); whereas for rock and roll, or hard rock which contains a lot of noise, the beats are more confused and 'C' should be low (about 1.1 or 1.0). There is a way, to make the algorithm determine automatically the good choice for the 'C' constant. We must compute the variance of the energies contained in the energy history buffer (E). This variance, which is nothing but the average of ( Energy Values – Energy average = (E) - <E> ), will quantify how marked the beats of the song are and thus will give us a way to compute the value of the 'C' constant. The formula to calculate the variance of the 43 E[i] values is described below (R4). Finally, the greater the variance is the more sensitive the algorithm should be and thus the smaller 'C' will become. We can choose a linear decrease of 'C' with 'V' (the variance) and for example when V → 200, C → 1.0 and when V → 25, C → 1.45 (R5). This is our new version of the sound energy beat detection algorithm:
Simple sound energy algorithm #3:
Every 1024 samples:
Those three algorithms were tested with several types of music, among others : pop, rock, metal, techno, rap, classical, punk. The fact is the results are quite unpredictable. I will only talk about Simple beat detection algorithm #3 as #2 and #1 are only pedagogical intermediates to get to the #3.
Clearly, the beat detection is very accurate and sounds right with techno and rap, the beats are very precise and the music contains very few noise. The algorithm is quite satisfying for that kind of music and if you aim to use beat detection on techno you can stop reading here, the rest won't change anything to your beat detection. However, even if the improvement of the dynamic 'C' calculations ameliorates things alot, the beat detection on punk, rock and hard rock, is sometimes quite approximate. We can feel it doesn't really get the rythm of the song. Indeed the algorithm detects energy peaks. Sometimes you can hear a drum beat which is sank among other noises and which goes trough the algorithm without being detected as a beat.
To explain this phenomena lets say a guitare and flute make alternatively an amplitude constant note. Each time the first finishes the other starts. The note made by the guitare and the note made by the flute have the same energy but the ear detects a certain rhythm because the notes of the instruments are at different pitch. For our algorithm (who is one might say colorblind) it is just an amplitude constant noise with no energy peaks. This partly explains why the algorithm doesn't detect precisely beats in songs with a lot of instruments playing at different rythms and simultaneously. Our next analysis will make us walk through this difficulty.
Comparing the results we have obtained with the Simple beat detection algorithm #3 to its computing cost, this algorithm is very efficient. If you are not looking for a perfect beat detection than I recommend you use it. Here is a screenshot of a program I made using this algorithm. You fill find the binaries and the sources on my homepage.
2 – Frequency selected sound energy
a - The idea and the algorithm
The issue with our last analysis of beat detection is that it is colorblind. We have seen that this could raise quite a few problems for noisy like songs in rock or pop music. What we must do is give our algorithm the abbility to determine on which frequency subband we have a beat and if it is powerful enough to take it into account. Basically we will try to detect big sound energy variations in particular frequency subbands, just like in our last analysis; unless this time we will be able to seperate beats regarding their color ( frequency subband ). Thus If we want to give more importants to low frequency beats or to high frequency beats it should be more easy. Notice that the energy computed in the time domain is the same as the energy computed in the frequency domain, so we don't have any difference between computing the energy in time domain or in frequency domain. For maths freaks this is called the Parseval Theorem.
Okay that was just a bit of sport, lets go back to the mainstream; Here is how the Frequency selected sound energy algorithm works: The source signals are still coming from (an) and (bn). (an) and (bn) can be taken from a wave file, or directly from a streaming microphon or line input. Each time we have accumulated 1024 new samples, we will pass to the frequency domain with a Fast Fourier Transform (FFT). We will thus obtain a 1024 frequency spectrum. We then divide this spectrum into however many subbands we like, here I will take 32. The more subbands you have, the more sensitive the algorithm will be but the harder it will become to adapt it to lots of different kinds of songs. Then we compute the sound energy contained in each of the subbands and we compare it to the recent energy average corresponding to this subband. If one or more subbands have an energy superior to their average we have detected a beat.
The great progress with the last algorithm is that we now know more about our beats, and thus we can use this information to change an animation, for example. So here is more precisely the Frequency selected sound energy algorithm #1:
Frequency selected sound energy algorithm #1:
Every 1024 samples:
To help out visualising how the data piles work have a look at this scheme:
Now the 'C' constant of this algorithm has nothing to do with the 'C' of the first algorithm, because we deal here with separated subbands the energy varies globally much more than with colorblind algorithms. Thus 'C' must be about 250. The results of this algorithm are convincing, it detects for example a symbal rhythm among other heavy noises in metal rock, and indeed the algorithm separates the signal into subbands, therefore the symbal rhythm cannot pass trough the algorithm without being recognized because it is isolated in the frequency domain from other sounds. However the complexity of the algorithm makes it useful only if you are dealing with very noisy sounds, in other cases, Simple beat detection algorithm #3 will do the job.
b - Enhancements and beat decision factors.
There are ways to enhance a bit more our Frequency selected sound energy algorithm #1.
First we will increase the number of subbands from 32 to 64. This will take obviously more computing time but it will also give us more precision in our beat detection. The second way to develop the accuracy of the algorithm uses the defaults of human ears. Human hearing system is not perfect; in fact its transfer function is more like a low pass filter. We hear more easily and more clearly low pitched noises than high pitch noises. This is why it is preferable to make a logarithmic repartition of the subbands. That is to say that subband 0 will contain only say 2 frequencies whereas the last subband, will contain say 20. More precisely the width 'wi' of the 'n' subbands indexed 'i' can be obtained using this argument:
Once you have equations (R11) and (R12) it is fairly easy to extract 'a' and 'b', and thus to find the law of the 'wi'. This calculus of 'a' and 'b' must be made manually and 'a' and 'b' defined as constants in the source; indeed they do not vary during the song.
So in fact in Frequency selected sound energy algorithm #1, all we have to modify is the number of subbands we will take equal to 64 and the (R7) relation. This relation becomes:
It may seem rather complicated but in fact it is not. Replacing this relation (R7) with (R7)' we have created Frequency selected sound energy algorithm #2. If you have musics with very tight and rapid beats, you may want to compute the stuff more frequently than every 1024 samples, but this is only for special cases, normally the beat should not be shorter than 1/40 of second.
Using the advantages of Frequency selected beat detection you can also enhance the beat decision factor. Up to now it was based on a simple comparison between the instant energy of the subband and the average energy of the subband. This algorithm enables you to decide beats differently. You may want for examples to cut beats which correspond to high pitch sounds if you run techno music or you may want to keep only [50-4000Hz] beats if you are working with speech signal. This algorithm has the advantage of being perfectly adaptable to any kind or category of signal which was not the case of Simple beat detection algorithm #3. Notice that the correspondants between index 'i' of the FFT transform and real frequency is given by formula :
So 'i' is the index of the value in the FFT output buffer, N is the size of the FFT transform (here 1024), fe is the sample frequency (here 44100). Thus index 256 corresponds to 10025 Hz. This formula may be useful if you want to create your own subbands and you want to know what the correspondants between indexes and real frequency are.
Another way of filtering the beats, or selecting them, is choosing only those which are marked and precise enough. As we have seen before, to detect the accuracy of beats we must compute the variance of the energy histories for each subband. If this variance is high it means that we have great differences of energy and thus that the beat are very intense. Thus all we have to do is compute this variance for each subband and add a test in the beat detection decision. To the "Es[i] > (C*<Ei>)" condition we will add "and V((Ei))>V0". V0 will be the variance limit, with experience 150 is a reasonable value. Now the V((Ei)) value is easy to compute, just follow the following equality if you don't see how :
The last (finally) way to enhance your beat detection, is to make the source signal pass through a derivation filter. Indeed differentiating the signal makes big variations of amplitude more marked and thus makes energy variations more marked and recognisable later in the algorithm. I haven't tried this optimisation but according to some sources this is quite useful. If you try it please give me your opinion on it!
Concerning the results of the Frequency selected sound energy algorithm #2 I must admit they are way more satisfying than the Simple sound energy algorithm #3. In a song the algorithm catches the bass beats as well as the tight cymbals hits. I insist on the fact that you may also select the beats in very different ways, which becomes quite useful if you know you are going to run techno music with low pitch beats for example. You may also select the beats differently according to there accuracy with the variance criteria. There are many other ways to decide beats; it is up to you to explore them and find the one which fits the most your needs.
I used Frequency selected sound energy algorithm #2algorithm in a demo program of which you can see some screenchots just below. One can see quite clearly that there is a beat in low frequencies (probably a bass or drum hit) and also a high pitch beat (probably a cymbal or such):
The reason why I called this part of the document, 'Statistical streaming beat detection', is that the energy can be considered as a random variable, of which we have been calculating values over time. Thus we could interpret those values as a sampling for the statistical analysis of a random variable. But one can push this approach further. When we have separated the energies histories into 128 subbands we created in fact 128 random energy variables. We can then apply some of the general statistical methods of analysis. For example, the principal components analysis method will enable you to determine if some of the subbands are directly linked or independent. This would help us to regroup some subbands which are directly linked and thus make the beat decision more efficient. However, this method is basically just far too computing expensive and maybe just too hard to implement comparing to the results we want. If you are looking for a really good challenge in beat detection you could push in this direction.