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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