# HG changeset patch
# User Chris Cannam
# Date 1352061650 0
#      Sun Nov 04 20:40:50 2012 +0000
# Node ID ddebd5730c64ee9767e9f153f03973022193d700
# Parent  c18641134f70ab95fe0da21c3d26f6c38e0c5442
Move explanatory comment to cpp file

diff --git a/src/MiniBpm.cpp b/src/MiniBpm.cpp
--- a/src/MiniBpm.cpp
+++ b/src/MiniBpm.cpp
@@ -21,6 +21,44 @@
     valid commercial licence before doing so.
 */
 
+/*
+ * Method:
+ *
+ * - Take the audio as a sequence of overlapping time-domain
+ *   frames. The frame size is chosen so that, following a Fourier
+ *   transform, the frequency range up to about an octave above
+ *   middle-C would take about half a dozen bins. This is a relatively
+ *   short frame giving quite good time resolution.
+ *
+ * - For each frame, extract the low-frequency range into the
+ *   frequency domain (up to a cutoff around 400-500 Hz) using a small
+ *   filterbank. Also extract a single bin from a high frequency range
+ *   (around 9K) for broadband noise, and calculate the overall RMS of
+ *   the frame.  (The low-frequency feature is the main contributor to
+ *   tempo estimation, the other two are used as fallbacks if there is
+ *   not enough low-frequency information.)  Accumulate sequences of
+ *   framewise spectral difference sums for the frequency domain
+ *   information, and a sequence of the RMS values, across the
+ *   duration of the audio.
+ *
+ * - When all audio has been processed, calculate an autocorrelation
+ *   of each of the three features normalised to unity maximum, and
+ *   calculate a weighted sum of the autocorrelations (discarding any
+ *   phase difference between the three signals) with the
+ *   low-frequency feature given the most weight.
+ *
+ * - Drag a comb filter across the subset of the summed
+ *   autocorrelation sequence that corresponds to the plausible tempo
+ *   range. Allocate to each lag a weighted sum of its value and those
+ *   of elements around beats-per-bar multiples of its lag.
+ *
+ * - Apply a simplistic perceptual weighting filter to prefer tempi
+ *   around 120-130bpm.
+ *
+ * - Find the peak of the resulting filtered autocorrelation and
+ *   return its corresponding tempo.
+ */
+
 #include "MiniBpm.h"
 
 #include <vector>
diff --git a/src/MiniBpm.h b/src/MiniBpm.h
--- a/src/MiniBpm.h
+++ b/src/MiniBpm.h
@@ -41,42 +41,6 @@
  * A single channel of audio only may be supplied (multi-channel is
  * not supported). To process multi-channel audio, average the
  * channels first.
- *
- * Method:
- *
- * - Take the audio as a sequence of overlapping time-domain
- *   frames. The frame size is chosen so that, following a Fourier
- *   transform, the frequency range up to about an octave above
- *   middle-C would take about half a dozen bins. This is a relatively
- *   short frame giving quite good time resolution.
- *
- * - For each frame, extract the low-frequency range into the
- *   frequency domain (up to a cutoff around 400-500 Hz) using a small
- *   filterbank. Also extract a single bin from a high frequency range
- *   (around 9K) for broadband noise, and calculate the overall RMS of
- *   the frame.  (The low-frequency feature is the main contributor to
- *   tempo estimation, the other two are used as fallbacks if there is
- *   not enough low-frequency information.)  Accumulate sequences of
- *   framewise spectral difference sums for the frequency domain
- *   information, and a sequence of the RMS values, across the
- *   duration of the audio.
- *
- * - When all audio has been processed, calculate an autocorrelation
- *   of each of the three features normalised to unity maximum, and
- *   calculate a weighted sum of the autocorrelations (discarding any
- *   phase difference between the three signals) with the
- *   low-frequency feature given the most weight.
- *
- * - Drag a comb filter across the subset of the summed
- *   autocorrelation sequence that corresponds to the plausible tempo
- *   range. Allocate to each lag a weighted sum of its value and those
- *   of elements around beats-per-bar multiples of its lag.
- *
- * - Apply a simplistic perceptual weighting filter to prefer tempi
- *   around 120-130bpm.
- *
- * - Find the peak of the resulting filtered autocorrelation and
- *   return its corresponding tempo.
  */
 class MiniBPM
 {