Changeset f0dd5fb for src/pitch


Ignore:
Timestamp:
Dec 7, 2013, 8:56:49 PM (10 years ago)
Author:
Paul Brossier <piem@piem.org>
Branches:
feature/autosink, feature/cnn, feature/cnn_org, feature/constantq, feature/crepe, feature/crepe_org, feature/pitchshift, feature/pydocstrings, feature/timestretch, fix/ffmpeg5, master, pitchshift, sampler, timestretch, yinfft+
Children:
fe6a393a
Parents:
846330f
Message:

src/pitch/pitch.h: improve documentation, adding a description of each method

File:
1 edited

Legend:

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  • src/pitch/pitch.h

    r846330f rf0dd5fb  
    2828/** \file
    2929
    30   Generic method for pitch detection
     30  Pitch detection object
    3131
    3232  This file creates the objects required for the computation of the selected
    3333  pitch detection algorithm and output the results, in midi note or Hz.
    3434
     35  \section pitch Pitch detection methods
     36
     37  A list of the pitch detection methods currently available follows.
     38
     39  \b \p default : use the default method
     40
     41  Currently, the default method is set to \p yinfft .
     42
     43  \b \p schmitt : Schmitt trigger
     44
     45  This pitch extraction method implements a Schmitt trigger to estimate the
     46  period of a signal.
     47
     48  This file was derived from the tuneit project, written by Mario Lang to
     49  detect the fundamental frequency of a sound.
     50
     51  See http://delysid.org/tuneit.html
     52
     53  \b \p fcomb : a fast harmonic comb filter
     54
     55  This pitch extraction method implements a fast harmonic comb filter to
     56  determine the fundamental frequency of a harmonic sound.
     57
     58  This file was derived from the tuneit project, written by Mario Lang to
     59  detect the fundamental frequency of a sound.
     60
     61  See http://delysid.org/tuneit.html
     62
     63  \b \p mcomb : multiple-comb filter
     64
     65  This fundamental frequency estimation algorithm implements spectral
     66  flattening, multi-comb filtering and peak histogramming.
     67
     68  This method was designed by Juan P. Bello and described in:
     69
     70  Juan-Pablo Bello. ``Towards the Automated Analysis of Simple Polyphonic
     71  Music''.  PhD thesis, Centre for Digital Music, Queen Mary University of
     72  London, London, UK, 2003.
     73
     74  \b \p yin : YIN algorithm
     75
     76  This algorithm was developped by A. de Cheveigne and H. Kawahara and
     77  published in:
     78
     79  De Cheveigné, A., Kawahara, H. (2002) "YIN, a fundamental frequency
     80  estimator for speech and music", J. Acoust. Soc. Am. 111, 1917-1930.
     81
     82  see http://recherche.ircam.fr/equipes/pcm/pub/people/cheveign.html
     83
     84  \b \p yinfft : Yinfft algorithm
     85
     86  This algorithm was derived from the YIN algorithm. In this implementation, a
     87  Fourier transform is used to compute a tapered square difference function,
     88  which allows spectral weighting. Because the difference function is tapered,
     89  the selection of the period is simplified.
     90
     91  Paul Brossier, [Automatic annotation of musical audio for interactive
     92  systems](http://aubio.org/phd/), Chapter 3, Pitch Analysis, PhD thesis,
     93  Centre for Digital music, Queen Mary University of London, London, UK, 2006.
     94
    3595  \example pitch/test-pitch.c
     96  \example examples/aubiopitch.c
    3697
    3798*/
     
    71132  \param samplerate sampling rate of the signal
    72133
     134  \return newly created ::aubio_pitch_t
     135
    73136*/
    74137aubio_pitch_t *new_aubio_pitch (char_t * method,
    75138    uint_t buf_size, uint_t hop_size, uint_t samplerate);
    76139
    77 /** set the output unit of the pitch detection object 
     140/** set the output unit of the pitch detection object
    78141
    79142  \param o pitch detection object as returned by new_aubio_pitch()
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