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#ifndef INCLUDE_F0ESTIMATE_H
#define INCLUDE_F0ESTIMATE_H
/*
* This is the Loris C++ Class Library, implementing analysis,
* manipulation, and synthesis of digitized sounds using the Reassigned
* Bandwidth-Enhanced Additive Sound Model.
*
* Loris is Copyright (c) 1999-2010 by Kelly Fitz and Lippold Haken
*
* 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
*
*
* F0Estimate.h
*
* Implementation of an iterative alrogithm for computing an
* estimate of fundamental frequency from a sequence of sinusoidal
* frequencies and amplitudes using a likelihood estimator
* adapted from Quatieri's Speech Signal Processing text. The
* algorithm here takes advantage of the fact that spectral peaks
* have already been identified and extracted in the analysis/modeling
* process.
*
* Kelly Fitz, 28 March 2006
* loris@cerlsoundgroup.org
*
* http://www.cerlsoundgroup.org/Loris/
*
*/
#include <vector>
// begin namespace
namespace Loris {
// ---------------------------------------------------------------------------
// class F0Estimate
//
//! Represents a configuration of an iterative alrogithm for computing an
//! estimate of fundamental frequency from a sequence of sinusoidal
//! frequencies and amplitudes using a likelihood estimator adapted
//! from Quatieri's Speech Signal Processing text. This algorithm takes
//! advantage of the fact that spectral peaks have already been identified
//! and extracted in the analysis/modeling process.
//!
//! The algorithm consists of the following steps:
//! 1) Identify candidate F0s as the integer divisors of the sinusoidal
//! frequencies provided, within the specified range (this algorithm
//! relies on the reasonable assumption that for any frequency recognized
//! as a likely F0, at least one of the sinusoidal frequencies must
//! represent a harmonic, the likelihood function makes this same
//! assumption)
//! 2) Select the highest frequency candidate (within range) that maximizes
//! the likelihood function (because all subharmonics of the true F0 will
//! be equal in likelihood to the true F0, but no higher frequency can
//! be as likely).
//! 2a) Check the likelihood of integer multiples of the best candidate,
//! choose the highest multiple (within the specified range) that
//! as likely as the best candidate frequency to be the new best
//! candidate.
//! 3) Refine the best candidate using the secant method for refining the
//! root of the derivative of the likelihood function in the neighborhood
//! of the best candidate (because a peak in the likelihood function is
//! a root of the derivative of that function).
//
class F0Estimate
{
private:
double m_frequency; //! estimated fundamental frequency in Hz
double m_confidence; //! normalized confidence for this estimate,
//! equal to 1.0 when all frequencies are perfect
//! harmonics of this estimate's frequency
public:
// --- lifecycle ---
//! Construct from parameters of the iterative F0 estimation
//! algorithm. Find candidate F0 estimates as integer divisors
//! of the peak frequencies, pick the highest frequency of the
//! most likely candidates, and refine that estiamte using the
//! secant method.
//!
//! Store the frequency and the normalized value of the
//! likelihood function at that frequency (1.0 indicates that
//! all the peaks are perfect harmonics of the estimated
//! frequency).
F0Estimate( const std::vector<double> & amps,
const std::vector<double> & freqs,
double fmin, double fmax,
double resolution );
// default copy/assign/destroy are OK
// Not sure whether or why these would be useful.
//
// F0Estimate( void ) : m_frequency( 0 ), m_confidence( 0 ) {}
// F0Estimate( double f, double c ) : m_frequency( f ), m_confidence( c ) {}
// --- accessors ---
//! Return the F0 frequency estimate, in Hz, for this estimate.
double frequency( void ) const { return m_frequency; }
//! Return the normalized confidence for this estimate,
//! equal to 1.0 when all frequencies are perfect
//! harmonics of this estimate's frequency.
double confidence( void ) const { return m_confidence; }
}; // end of class F0Estimate
} // end of namespace Loris
#endif // ndef INCLUDE_F0ESTIMATE_H
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