BFCC¶

streaming mode | Spectral category

Inputs¶

spectrum (vector_real) - the audio spectrum

Outputs¶

bands (vector_real) - the energies in bark bands

bfcc (vector_real) - the bark frequency cepstrum coefficients

Parameters¶

dctType (integer ∈ [2, 3], default = 2) :
the DCT type

highFrequencyBound (real ∈ (0, ∞), default = 11000) :
the upper bound of the frequency range [Hz]

inputSize (integer ∈ (1, ∞), default = 1025) :
the size of input spectrum

liftering (integer ∈ [0, ∞), default = 0) :
the liftering coefficient. Use ‘0’ to bypass it

logType (string ∈ {natural, dbpow, dbamp, log}, default = dbamp) :
logarithmic compression type. Use ‘dbpow’ if working with power and ‘dbamp’ if working with magnitudes

lowFrequencyBound (real ∈ [0, ∞), default = 0) :
the lower bound of the frequency range [Hz]

normalize (string ∈ {unit_sum, unit_max}, default = unit_sum) :
‘unit_max’ makes the vertex of all the triangles equal to 1, ‘unit_sum’ makes the area of all the triangles equal to 1

numberBands (integer ∈ [1, ∞), default = 40) :
the number of bark bands in the filter

numberCoefficients (integer ∈ [1, ∞), default = 13) :
the number of output cepstrum coefficients

sampleRate (real ∈ (0, ∞), default = 44100) :
the sampling rate of the audio signal [Hz]

type (string ∈ {magnitude, power}, default = power) :
use magnitude or power spectrum

weighting (string ∈ {warping, linear}, default = warping) :
type of weighting function for determining triangle area

Description¶

This algorithm computes the bark-frequency cepstrum coefficients of a spectrum. Bark bands and their subsequent usage in cepstral analysis have shown to be useful in percussive content [1, 2] This algorithm is implemented using the Bark scaling approach in the Rastamat version of the MFCC algorithm and in a similar manner to the MFCC-FB40 default specs:

http://www.ee.columbia.edu/ln/rosa/matlab/rastamat/

filterbank of 40 bands from 0 to 11000Hz

take the log value of the spectrum energy in each bark band

DCT of the 40 bands down to 13 mel coefficients

The parameters of this algorithm can be configured in order to behave like Rastamat [3] as follows:

type = ‘power’

weighting = ‘linear’

lowFrequencyBound = 0

highFrequencyBound = 8000

numberBands = 26

numberCoefficients = 13

normalize = ‘unit_max’

dctType = 3

logType = ‘log’

liftering = 22

In order to completely behave like Rastamat the audio signal has to be scaled by 2^15 before the processing and if the Windowing and FrameCutter algorithms are used they should also be configured as follows.

FrameGenerator:

frameSize = 1102

hopSize = 441

startFromZero = True

validFrameThresholdRatio = 1

Windowing:

type = ‘hann’

size = 1102

zeroPadding = 946

normalized = False

This algorithm depends on the algorithms TriangularBarkBands (not the regular BarkBands algo as it is non-configurable) and DCT and therefore inherits their parameter restrictions. An exception is thrown if any of these restrictions are not met. The input “spectrum” is passed to the TriangularBarkBands algorithm and thus imposes TriangularBarkBands’ input requirements. Exceptions are inherited by TriangualrBarkBands as well as by DCT.

References:: [1] P. Herrera, A. Dehamel, and F. Gouyon, “Automatic labeling of unpitched percussion sounds in Audio Engineering Society 114th Convention, 2003, [2] W. Brent, “Cepstral Analysis Tools for Percussive Timbre Identification in Proceedings of the 3rd International Pure Data Convention, Sao Paulo, Brazil, 2009,

Source code¶

C++ source code

C++ header file