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Diffstat (limited to 'src/loris/F0Estimate.h')
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diff --git a/src/loris/F0Estimate.h b/src/loris/F0Estimate.h new file mode 100644 index 0000000..a20e81e --- /dev/null +++ b/src/loris/F0Estimate.h @@ -0,0 +1,135 @@ +#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 |