Average Mutual Information
can be used as a method for beat tracking.
One of the main problems addressed in any research related to beat
detection is the quantitative evaluation of tracking accuracy, in particular for
music files. In this study and in most
of the studies reviewed, human interaction is required for the measure of
performance in a beat tracking system such as counting the number of beats in a
period of time or labeling the beat positions in an audio file. Most of the studies in beat detection
measure the beat tracking accuracy using their real-time systems over 40 or 50
songs of different genres [12], [31], and determine if the system performed
well or not. In this study several
types of audio files were tested to determine if AMI could be applicable in
solving the beat detection problem.
The system performed better
with decimation factor N=2, maxtau=2, bin=64, fs=8 kHz and length of 5
sec. The algorithm of detection is very
simple and if it is implemented in C++ the response times could be very
suitable for real-time implementations.
In addition, if a proprietary function of AMI were implemented just for
beat detection instead of using the one provided by TSTOOLS, the program could
run faster and probably yield better results.
The next stage of this research is the implementation of a real-time
system that could use the method presented (which detects the beat rate), but
the way to determine the beat phase or when exactly the next beat is going to
happen, needs to be defined.
The system also presented
some capacity to detect quality in the beats as the quarter note or the strong
beat (half note). If this algorithm is
combined with others, which handle hypothesis agents as the Goto and Muraoka
[12], it could generate some interesting improvements. Also AMI should be tried in beat detection
algorithms that already use autocorrelation in some of their states, as Goto
and Muraoka [12], because audio signals are more non-linear.
Further
experiments could be achieved by adding algorithms capable of making decisions
involving the beat detection values yielded by the AMI. More intelligent algorithms are required to
interpret AMI results. But even human
beings, whose own innate rhythm recognition skills could have subjective
appreciation and different perception depending if they are a trained musician
or just a novice, are prone to error in accomplishing this difficult perceptual
task.