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/*
* Copyright (c) 2001-2004 MUSIC TECHNOLOGY GROUP (MTG)
* UNIVERSITAT POMPEU FABRA
*
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*
*/
#ifndef __SpectralDescriptors_H__
#define __SpectralDescriptors_H__
#include "Array.hxx"
#include "Descriptor.hxx"
#include "Spectrum.hxx"
/*
* This class holds Descriptors computed from Spectral data
*
*
*/
namespace CLAM {
class SpectralDescriptors : public Descriptor {
public:
DYNAMIC_TYPE_USING_INTERFACE (SpectralDescriptors, 21, Descriptor);
/** The spectral power mean value.
* The unit of this measure can be dB
* or none, depending on the scale set for the
* measured Spectrum object.
* @see Spectrum::SetScale
* @see EScale
* @see Stats::GetMean
*/
DYN_ATTRIBUTE (0, public, TData, Mean);
/**
* The geometric mean for the spectral power values sequence.
* See <a href="http://mathworld.wolfram.com/GeometricMean.html">this</a> for a definition of
* this pythagorean mean. Note that computing this measurement over long sequences of
* small real numbers ( as the ones one usually founds in spectral power distributions derived
* of audio windowed with a normalized window function ) pose a numerical problem. To avoid
* this, computation of Geometric mean is restricted to Log scale Spectral Power Distributions
* since this allows to change the product for a summation.
*
* This measure is expressed in dBs.
* @see Stats::GetGeometricMean
*/
DYN_ATTRIBUTE (1, public, TData, GeometricMean);
/**
* The squared sum of spectral power distribution values.
* This measure comes in the same units as the distribution
* values.
* @see Stats::GetEnergy
*/
DYN_ATTRIBUTE (2, public, TData, Energy);
/**
* The frequency where the center of mass of the spectral power
* distribution lies.
* This measure is expressed in Hz.
*
* @see Stats::GetCentroid
*/
DYN_ATTRIBUTE (3, public, TData, Centroid);
DYN_ATTRIBUTE (4, public, TData, Moment2);
DYN_ATTRIBUTE (5, public, TData, Moment3);
DYN_ATTRIBUTE (6, public, TData, Moment4);
DYN_ATTRIBUTE (7, public, TData, Moment5);
DYN_ATTRIBUTE (8, public, TData, Moment6);
DYN_ATTRIBUTE (9, public, TData, Flatness);
DYN_ATTRIBUTE (10,public, TData, MagnitudeKurtosis);
DYN_ATTRIBUTE (11,public, Array<TData>, MFCC);
/**
* Frequency of the maximum magnitude of the spectrum
* normalized by the spectral range
*/
DYN_ATTRIBUTE (12,public, TData, MaxMagFreq);
/**
* The ratio between the energy over 0-100 Hz band and the whole spectrum energy.
* To avoid singularities while keeping descriptor continuity,
* when the whole spectrum energy drops bellow $10^{-4}$,
* such value is considered as whole spectrum energy.
*/
DYN_ATTRIBUTE (13,public, TData, LowFreqEnergyRelation);
/**
* The spectral spread is the variation of the spectrum
* around its mean value. It's computed from the second
* order moment.
*/
DYN_ATTRIBUTE (14,public, TData, Spread);
DYN_ATTRIBUTE (15,public, TData, MagnitudeSkewness);
/**
* The spectral roll-off point is the frequency value
* so that the 85% of the spectral energy is contained below
* it. For silences this is 0Hz. Measured in Hz.
*
* \f[
* Rolloff / \sum_{f=0}^{RollOff} {a_f^2} = 0.85 \times \sum_{f=0}^{SpectralRange} {a_f^2}
* \f]
*/
DYN_ATTRIBUTE (16,public, TData, Rolloff);
/**
* The spectral slope represents the amount of decreasing of
* the spectral magnitude. Measured in ??.
* @see Stats::Slope
*/
DYN_ATTRIBUTE (17,public, TData, Slope);
/**
* Sum of the squared spectrum magnitude multiplied by the wave number of the bin.
* It could be considered the energy derivative, a high pass filter,
* which gives higher values for high frequency content.
*
* \f[
* HighFrequencyContent = \sum_{i=0}^{nBins} i magnitude_i^2
* \f]
*/
DYN_ATTRIBUTE (18,public, TData, HighFrequencyContent);
DYN_ATTRIBUTE (19,public, Array<SpectralDescriptors>, BandDescriptors);
DYN_ATTRIBUTE (20,public, Array<TData>,PCP);
public:
SpectralDescriptors(Spectrum* pSpectrum);
SpectralDescriptors(TData initVal);
const Spectrum* GetpSpectrum() const;
void SetpSpectrum(Spectrum* pSpectrum);
void ConcreteCompute();
//XA_C2S private:
void DefaultInit();
void CopyInit(const SpectralDescriptors & copied);
TData ComputeSpectralFlatness();
TData ComputeHighFrequencyContent();
TData ComputeMaxMagFreq();
TData ComputeLowFreqEnergyRelation();
TData ComputeRolloff();
TData ComputeSpread();
TData ComputeSlope();
private:
const Spectrum* mpSpectrum;
/** Conversion from index to frequency, needed for many descriptors */
double mDeltaFreq; // double because a lot of computations depends on its precission
};
SpectralDescriptors operator * (const SpectralDescriptors& a,TData mult);
SpectralDescriptors operator * (TData mult,const SpectralDescriptors& a);
SpectralDescriptors operator / (const SpectralDescriptors& a,TData div);
SpectralDescriptors operator * (const SpectralDescriptors& a,const SpectralDescriptors& b) ;
SpectralDescriptors operator + (const SpectralDescriptors& a, const SpectralDescriptors& b);
template<>
inline SpectralDescriptors CLAM_max (const SpectralDescriptors& a,const SpectralDescriptors& b)
{
SpectralDescriptors tmpD(a);
if(a.HasMean() && b.HasMean() )
{
if(b.GetMean()>a.GetMean())
tmpD.SetMean(b.GetMean());
}
if(a.HasGeometricMean() && b.HasGeometricMean() )
{
if(b.GetGeometricMean()>a.GetGeometricMean())
tmpD.SetGeometricMean(b.GetGeometricMean());
}
if(a.HasEnergy() && b.HasEnergy() )
{
if(b.GetEnergy()>a.GetEnergy())
tmpD.SetEnergy(b.GetEnergy());
}
if(a.HasCentroid() && b.HasCentroid() )
{
if(b.GetCentroid()>a.GetCentroid())
tmpD.SetCentroid(b.GetCentroid());
}
if(a.HasMoment2() && b.HasMoment2() )
{
if(b.GetMoment2()>a.GetMoment2())
tmpD.SetMoment2(b.GetMoment2());
}
if(a.HasMoment3() && b.HasMoment3() )
{
if(b.GetMoment3()>a.GetMoment3())
tmpD.SetMoment3(b.GetMoment3());
}
if(a.HasMoment4() && b.HasMoment4() )
{
if(b.GetMoment4()>a.GetMoment4())
tmpD.SetMoment4(b.GetMoment4());
}
if(a.HasMoment5() && b.HasMoment5())
{
if(b.GetMoment5()>a.GetMoment5())
tmpD.SetMoment5(b.GetMoment5());
}
if(a.HasMoment6() && b.HasMoment6() )
{
if(b.GetMoment6()>a.GetMoment6())
tmpD.SetMoment6(b.GetMoment6());
}
if(a.HasFlatness() && b.HasFlatness() )
{
if(b.GetFlatness()>a.GetFlatness())
tmpD.SetFlatness(b.GetFlatness());
}
if(a.HasMagnitudeKurtosis() && b.HasMagnitudeKurtosis() )
{
if(b.GetMagnitudeKurtosis()>a.GetMagnitudeKurtosis())
tmpD.SetMagnitudeKurtosis(b.GetMagnitudeKurtosis());
}
if(a.HasMaxMagFreq() && b.HasMaxMagFreq() )
{
if(b.GetMaxMagFreq()>a.GetMaxMagFreq())
tmpD.SetMaxMagFreq(b.GetMaxMagFreq());
}
if(a.HasLowFreqEnergyRelation() && b.HasLowFreqEnergyRelation() )
{
if(b.GetLowFreqEnergyRelation()>a.GetLowFreqEnergyRelation())
tmpD.SetLowFreqEnergyRelation(b.GetLowFreqEnergyRelation());
}
if(a.HasSpread() && b.HasSpread() )
{
if(b.GetSpread()>a.GetSpread())
tmpD.SetSpread(b.GetSpread());
}
if(a.HasMagnitudeSkewness() && b.HasMagnitudeSkewness() )
{
if(b.GetMagnitudeSkewness()>a.GetMagnitudeSkewness())
tmpD.SetMagnitudeSkewness(b.GetMagnitudeSkewness());
}
if(a.HasRolloff() && b.HasRolloff() )
{
if(b.GetRolloff()>a.GetRolloff())
tmpD.SetRolloff(b.GetRolloff());
}
if(a.HasSlope() && b.HasSlope() )
{
if(b.GetSlope()>a.GetSlope())
tmpD.SetSlope(b.GetSlope());
}
if(a.HasHighFrequencyContent() && b.HasHighFrequencyContent() )
{
if(b.GetHighFrequencyContent()>a.GetHighFrequencyContent())
tmpD.SetHighFrequencyContent(b.GetHighFrequencyContent());
}
if(a.HasBandDescriptors() && b.HasBandDescriptors() )
{
/* Array does not have these operators
if(b.GetBandDescriptors()>a.GetBandDescriptors())
tmpD.SetBandDescriptors(b.GetBandDescriptors() );*/
}
if(a.HasMFCC() && b.HasMFCC() )
{
/* Array does not have these operators
if(b.GetMFCC()>a.GetMFCC())
tmpD.SetMFCC(b.GetMFCC());*/
}
return tmpD;
}
template<>
inline SpectralDescriptors CLAM_min (const SpectralDescriptors& a,const SpectralDescriptors& b)
{
SpectralDescriptors tmpD(a);
if(a.HasMean() && b.HasMean() )
{
if(b.GetMean()<a.GetMean())
tmpD.SetMean(b.GetMean());
}
if(a.HasGeometricMean() && b.HasGeometricMean() )
{
if(b.GetGeometricMean()<a.GetGeometricMean())
tmpD.SetGeometricMean(b.GetGeometricMean());
}
if(a.HasEnergy() && b.HasEnergy() )
{
if(b.GetEnergy()<a.GetEnergy())
tmpD.SetEnergy(b.GetEnergy());
}
if(a.HasCentroid() && b.HasCentroid() )
{
if(b.GetCentroid()<a.GetCentroid())
tmpD.SetCentroid(b.GetCentroid());
}
if(a.HasMoment2() && b.HasMoment2() )
{
if(b.GetMoment2()<a.GetMoment2())
tmpD.SetMoment2(b.GetMoment2());
}
if(a.HasMoment3() && b.HasMoment3() )
{
if(b.GetMoment3()<a.GetMoment3())
tmpD.SetMoment3(b.GetMoment3());
}
if(a.HasMoment4() && b.HasMoment4() )
{
if(b.GetMoment4()<a.GetMoment4())
tmpD.SetMoment4(b.GetMoment4());
}
if(a.HasMoment5() && b.HasMoment5())
{
if(b.GetMoment5()<a.GetMoment5())
tmpD.SetMoment5(b.GetMoment5());
}
if(a.HasMoment6() && b.HasMoment6() )
{
if(b.GetMoment6()<a.GetMoment6())
tmpD.SetMoment6(b.GetMoment6());
}
if(a.HasFlatness() && b.HasFlatness() )
{
if(b.GetFlatness()<a.GetFlatness())
tmpD.SetFlatness(b.GetFlatness());
}
if(a.HasMagnitudeKurtosis() && b.HasMagnitudeKurtosis() )
{
if(b.GetMagnitudeKurtosis()<a.GetMagnitudeKurtosis())
tmpD.SetMagnitudeKurtosis(b.GetMagnitudeKurtosis());
}
if(a.HasMaxMagFreq() && b.HasMaxMagFreq() )
{
if(b.GetMaxMagFreq()<a.GetMaxMagFreq())
tmpD.SetMaxMagFreq(b.GetMaxMagFreq());
}
if(a.HasLowFreqEnergyRelation() && b.HasLowFreqEnergyRelation() )
{
if(b.GetLowFreqEnergyRelation()<a.GetLowFreqEnergyRelation())
tmpD.SetLowFreqEnergyRelation(b.GetLowFreqEnergyRelation());
}
if(a.HasSpread() && b.HasSpread() )
{
if(b.GetSpread()<a.GetSpread())
tmpD.SetSpread(b.GetSpread());
}
if(a.HasMagnitudeSkewness() && b.HasMagnitudeSkewness() )
{
if(b.GetMagnitudeSkewness()<a.GetMagnitudeSkewness())
tmpD.SetMagnitudeSkewness(b.GetMagnitudeSkewness());
}
if(a.HasRolloff() && b.HasRolloff() )
{
if(b.GetRolloff()<a.GetRolloff())
tmpD.SetRolloff(b.GetRolloff());
}
if(a.HasSlope() && b.HasSlope() )
{
if(b.GetSlope()<a.GetSlope())
tmpD.SetSlope(b.GetSlope());
}
if(a.HasHighFrequencyContent() && b.HasHighFrequencyContent() )
{
if(b.GetHighFrequencyContent()<a.GetHighFrequencyContent())
tmpD.SetHighFrequencyContent(b.GetHighFrequencyContent());
}
if(a.HasBandDescriptors() && b.HasBandDescriptors() )
{
/* Array does not have these operators
if(b.GetBandDescriptors()<a.GetBandDescriptors())
tmpD.SetBandDescriptors(b.GetBandDescriptors() );*/
}
if(a.HasMFCC() && b.HasMFCC() )
{
/* Array does not have these operators
if(b.GetMFCC()<a.GetMFCC())
tmpD.SetMFCC(b.GetMFCC());*/
}
return tmpD;
}
}
#endif /* __SpectralDescriptors_H__ */
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