annotate base/Window.h @ 298:255e431ae3d4

* Key detector: when returning key strengths, use the peak value of the
  three underlying chromagram correlations (from 36-bin chromagram)
  corresponding to each key, instead of the mean.
  Rationale: This is the same method as used when returning the key value,
  and it's nice to have the same results in both returned value and plot.
  The peak performed better than the sum with a simple test set of triads,
  so it seems reasonable to change the plot to match the key output rather
  than the other way around.

* FFT: kiss_fftr returns only the non-conjugate bins, synthesise the rest
  rather than leaving them (perhaps dangerously) undefined.  Fixes an
  uninitialised data error in chromagram that could cause garbage results
  from key detector.

* Constant Q: remove precalculated values again, I reckon they're not
  proving such a good tradeoff.

/* -*- c-basic-offset: 4 indent-tabs-mode: nil -*-  vi:set ts=8 sts=4 sw=4: */

/*
    QM DSP library
    Centre for Digital Music, Queen Mary, University of London.
    This file Copyright 2006 Chris Cannam.
    All rights reserved.
*/

#ifndef _WINDOW_H_
#define _WINDOW_H_

#include <cmath>
#include <iostream>
#include <map>

enum WindowType {
    RectangularWindow,
    BartlettWindow,
    HammingWindow,
    HanningWindow,
    BlackmanWindow,
    GaussianWindow,
    ParzenWindow
};

template <typename T>
class Window
{
public:
    /**
     * Construct a windower of the given type.
     */
    Window(WindowType type, size_t size) : m_type(type), m_size(size) { encache(); }
    Window(const Window &w) : m_type(w.m_type), m_size(w.m_size) { encache(); }
    Window &operator=(const Window &w) {
	if (&w == this) return *this;
	m_type = w.m_type;
	m_size = w.m_size;
	encache();
	return *this;
    }
    virtual ~Window() { delete[] m_cache; }
    
    void cut(T *src) const { cut(src, src); }
    void cut(const T *src, T *dst) const {
	for (size_t i = 0; i < m_size; ++i) dst[i] = src[i] * m_cache[i];
    }

    WindowType getType() const { return m_type; }
    size_t getSize() const { return m_size; }

protected:
    WindowType m_type;
    size_t m_size;
    T *m_cache;
    
    void encache();
};

template <typename T>
void Window<T>::encache()
{
    size_t n = m_size;
    T *mult = new T[n];
    size_t i;
    for (i = 0; i < n; ++i) mult[i] = 1.0;

    switch (m_type) {
		
    case RectangularWindow:
	for (i = 0; i < n; ++i) {
	    mult[i] = mult[i] * 0.5;
	}
	break;
	    
    case BartlettWindow:
	for (i = 0; i < n/2; ++i) {
	    mult[i] = mult[i] * (i / T(n/2));
	    mult[i + n/2] = mult[i + n/2] * (1.0 - (i / T(n/2)));
	}
	break;
	    
    case HammingWindow:
	for (i = 0; i < n; ++i) {
	    mult[i] = mult[i] * (0.54 - 0.46 * cos(2 * M_PI * i / n));
	}
	break;
	    
    case HanningWindow:
	for (i = 0; i < n; ++i) {
	    mult[i] = mult[i] * (0.50 - 0.50 * cos(2 * M_PI * i / n));
	}
	break;
	    
    case BlackmanWindow:
	for (i = 0; i < n; ++i) {
	    mult[i] = mult[i] * (0.42 - 0.50 * cos(2 * M_PI * i / n)
				 + 0.08 * cos(4 * M_PI * i / n));
	}
	break;
	    
    case GaussianWindow:
	for (i = 0; i < n; ++i) {
	    mult[i] = mult[i] * exp((-1.0 / (n*n)) * ((T(2*i) - n) *
						      (T(2*i) - n)));
	}
	break;
	    
    case ParzenWindow:
	for (i = 0; i < n; ++i) {
	    mult[i] = mult[i] * (1.0 - fabs((T(2*i) - n) / T(n + 1)));
	}
	break;
    }
	
    m_cache = mult;
}

#endif