/* Copyright (C) 2003-2009 Paul Brossier This file is part of aubio. aubio 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 3 of the License, or (at your option) any later version. aubio 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 aubio. If not, see . */ /** \file Spectral description functions All of the following spectral description functions take as arguments the FFT of a windowed signal (as created with aubio_pvoc). They output one smpl_t per buffer (stored in a vector of size [1]). \section specdesc Spectral description functions A list of the spectral description methods currently available follows. \subsection onsetdesc Onset detection functions These functions are designed to raise at notes attacks in music signals. \b \p energy : Energy based onset detection function This function calculates the local energy of the input spectral frame. \b \p hfc : High Frequency Content onset detection function This method computes the High Frequency Content (HFC) of the input spectral frame. The resulting function is efficient at detecting percussive onsets. Paul Masri. Computer modeling of Sound for Transformation and Synthesis of Musical Signal. PhD dissertation, University of Bristol, UK, 1996. \b \p complex : Complex Domain Method onset detection function Christopher Duxbury, Mike E. Davies, and Mark B. Sandler. Complex domain onset detection for musical signals. In Proceedings of the Digital Audio Effects Conference, DAFx-03, pages 90-93, London, UK, 2003. \b \p phase : Phase Based Method onset detection function Juan-Pablo Bello, Mike P. Davies, and Mark B. Sandler. Phase-based note onset detection for music signals. In Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing, pages 441­444, Hong-Kong, 2003. \b \p specdiff : Spectral difference method onset detection function Jonhatan Foote and Shingo Uchihashi. The beat spectrum: a new approach to rhythm analysis. In IEEE International Conference on Multimedia and Expo (ICME 2001), pages 881­884, Tokyo, Japan, August 2001. \b \p kl : Kullback-Liebler onset detection function Stephen Hainsworth and Malcom Macleod. Onset detection in music audio signals. In Proceedings of the International Computer Music Conference (ICMC), Singapore, 2003. \b \p mkl : Modified Kullback-Liebler onset detection function Paul Brossier, ``Automatic annotation of musical audio for interactive systems'', Chapter 2, Temporal segmentation, PhD thesis, Centre for Digital music, Queen Mary University of London, London, UK, 2006. \b \p specflux : Spectral Flux Simon Dixon, Onset Detection Revisited, in ``Proceedings of the 9th International Conference on Digital Audio Effects'' (DAFx-06), Montreal, Canada, 2006. \subsection shapedesc Spectral shape descriptors The following descriptors are described in: Geoffroy Peeters, A large set of audio features for sound description (similarity and classification) in the CUIDADO project, CUIDADO I.S.T. Project Report 2004 (pdf) \b \p centroid : Spectral centroid The spectral centroid represents the barycenter of the spectrum. \e Note: This function returns the result in bin. To get the spectral centroid in Hz, aubio_bintofreq() should be used. \b \p spread : Spectral spread The spectral spread is the variance of the spectral distribution around its centroid. See also Standard deviation on Wikipedia. \b \p skewness : Spectral skewness Similarly, the skewness is computed from the third order moment of the spectrum. A negative skewness indicates more energy on the lower part of the spectrum. A positive skewness indicates more energy on the high frequency of the spectrum. See also Skewness on Wikipedia. \b \p kurtosis : Spectral kurtosis The kurtosis is a measure of the flatness of the spectrum, computed from the fourth order moment. See also Kurtosis on Wikipedia. \b \p slope : Spectral slope The spectral slope represents decreasing rate of the spectral amplitude, computed using a linear regression. \b \p decrease : Spectral decrease The spectral decrease is another representation of the decreasing rate, based on perceptual criteria. \b \p rolloff : Spectral roll-off This function returns the bin number below which 95% of the spectrum energy is found. \example spectral/test-specdesc.c */ #ifndef ONSETDETECTION_H #define ONSETDETECTION_H #ifdef __cplusplus extern "C" { #endif /** spectral description structure */ typedef struct _aubio_specdesc_t aubio_specdesc_t; /** execute spectral description function on a spectral frame Generic function to compute spectral detescription. \param o spectral description object as returned by new_aubio_specdesc() \param fftgrain input signal spectrum as computed by aubio_pvoc_do \param desc output vector (one sample long, to send to the peak picking) */ void aubio_specdesc_do (aubio_specdesc_t * o, cvec_t * fftgrain, fvec_t * desc); /** creation of a spectral description object \param method spectral description method \param buf_size length of the input spectrum frame The parameter \p method is a string that can be any of: - `energy`, `hfc`, `complex`, `phase`, `specdiff`, `kl`, `mkl`, `specflux` - `centroid`, `spread`, `skewness`, `kurtosis`, `slope`, `decrease`, `rolloff` */ aubio_specdesc_t *new_aubio_specdesc (char_t * method, uint_t buf_size); /** deletion of a spectral descriptor \param o spectral descriptor object as returned by new_aubio_specdesc() */ void del_aubio_specdesc (aubio_specdesc_t * o); #ifdef __cplusplus } #endif #endif /* ONSETDETECTION_H */