Minim 

BeatDetect FREQ_ENERGY SOUND_ENERGY dectectSize ( ) detect ( ) detectMode ( ) detectSize ( ) getDetectCenterFrequency ( ) isHat ( ) isKick ( ) isOnset ( ) isRange ( ) isSnare ( ) setSensitivity ( ) FFT BARTLETT BARTLETTHANN BLACKMAN COSINE GAUSS HAMMING HANN LANCZOS NONE TRIANGULAR avgSize ( ) calcAvg ( ) forward ( ) freqToIndex ( ) getAverageBandWidth ( ) getAverageCenterFrequency ( ) getAvg ( ) getBand ( ) getBandWidth ( ) getFreq ( ) getSpectrumImaginary ( ) getSpectrumReal ( ) indexToFreq ( ) inverse ( ) linAverages ( ) logAverages ( ) noAverages ( ) scaleBand ( ) scaleFreq ( ) setBand ( ) setFreq ( ) specSize ( ) timeSize ( ) window ( ) WindowFunction apply ( ) generateCurve ( ) 
The Analysis package contains classes for analyzing audio in realtime.
Fast Fourier Transform
A Fourier Transform is an algorithm that transforms a signal in the time
domain, such as a sample buffer, into a signal in the frequency domain, often
called the spectrum. The spectrum does not represent individual frequencies,
but actually represents frequency bands centered on particular frequencies.
The center frequency of each band is usually expressed as a fraction of the
sampling rate of the time domain signal and is equal to the index of the
frequency band divided by the total number of bands. The total number of
frequency bands is usually equal to the length of the time domain signal, but
access is only provided to frequency bands with indices less than half the
length, because they correspond to frequencies below the Nyquist frequency.
In other words, given a signal of length Beat (or Onset) Detection The BeatDetect class allows you to analyze an audio stream for beats (rhythmic onsets). Beat Detection Algorithms by Frederic Patin describes beats in the following way: 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.In fact, the two algorithms in this class are based on two algorithms described in that paper.
BeatDetect has two modes: sound energy tracking and frequency energy
tracking. In sound energy mode, the level of the buffer, as returned by
In sound energy mode you use 