calculation of a constant q spectral transform
DESCRIPTION
Calculation of a constant Q spectral transform. Judith C. Brown. Journal of the Acoustical Society of America,1991. Jain-De,Lee. Outline. INTRODUCTION CALCULATION RESULTS SUMMARY. INTRODUCTION. The work is based on the property that, for sounds made up of harmonic frequency components. - PowerPoint PPT PresentationTRANSCRIPT
Judith C. Brown
Journal of the Acoustical Society of America,1991
Jain-De,Lee
INTRODUCTION
CALCULATION
RESULTS
SUMMARY
The work is based on the property that, for sounds made up of harmonic frequency components
The positions of these frequency components relative to each other are the same independent of fundamental frequency
The conventional linear frequency representation
◦Rise to a constant separation◦Harmonic components vary with fundamental frequency
The result is that it is more difficult to pick out differences in other features
◦ Timbre◦Attack◦Decay
The log frequency representation
◦Constant pattern for the spectral components◦Recognizing a previously determined pattern becomes a
straightforward problem
The idea has theoretical appeal for its similarity to modern theories
◦ The perception of the pitch–Missing fundamental
To demonstrate the constant pattern for musical sound◦ The mapping of these data from the linear to the logarithmic
domain Too little information at low frequencies and too much
information at high frequencies
For example
◦Window of 1024 samples and sampling rate of 32000 samples/s and the resolution is 31.3 Hz(32000/1024=31.25)
The violin low end of the range is G3(196Hz) and the adjacent note is G#3(207.65 Hz),the resolution is much greater than the frequency separation for two adjacent notes tuned
The frequencies sampled by the discrete Fourier transform should be exponentially spaced
If we require quartertone spacing
◦ The variable resolution of at most ( 21/24 -1)= 0.03 times the frequency
◦A constant ratio of frequency to resolution f / δf = Q
◦Here Q =f /0.029f= 34
Quarter-tone spacing of the equal tempered scale ,the frequency of the k th spectral component is
The resolution f / δf for the DFT, then the window size must varied
fk = (21/24)k fmin
Where f an upper frequency chosen to be below the Nyquist frequency
fmin can be chosen to be the lowest frequency about which Information is desired
For quarter-tone resolution
Calculate the length of the window in frequency fk
Q = f / δf = f / 0.029f = 34
Where the quality factor Q is defined as f / δfbandwidth δf = f / QSampling rate S = 1/T
N[k]= S / δfk = (S / fk)Q
We obtain an expression for the k th spectral component for the constant Q transform
Hamming window that has the form
1
0
}/2exp{][][][N
n
NknjnxnWkX
1][
0
]}[/2exp{][],[][
1][
kN
n
kNQnjnxnkWkN
kX
W[k,n]=α + (1- α)cos(2πn/N[k])
Where α = 25/46 and 0 ≤ n ≤ N[k]-1
Constant Q transform of violinplaying diatonic scale pizzicato from G3 (196 Hz) to G5(784 Hz)
Constant Q transform of violinplaying D5(587 Hz) with vibratoConstant Q transform of violin glissando from D5 (587 Hz) to A5 (880Hz)Constant Q transform of flute playing diatonic scale from C4 (262 Hz) to C5 (523 Hz) with increasing amplitude
Constant Q transform of piano playing diatonic scale from C4 (262 Hz) to C5(523 Hz)The attack on D5(587 Hz) is also visible
Straightforward method of calculating a constant Q transform designed for musical representations
Waterfall plots of these data make it possible to visualize information present in digitized musical waveform