Ubuntu Manpage: aubioonset - a command line tool to extract musical onset times

Provided by: aubio-tools_0.4.9-4build1_amd64

NAME

       aubioonset - a command line tool to extract musical onset times

SYNOPSIS

       aubioonset source
       aubioonset [[-i] source] [-o sink]
                  [-r rate] [-B win] [-H hop]
                  [-O method] [-t thres]
                  [-T time-format]
                  [-s sil] [-m] [-f]
                  [-j] [-N miditap-note] [-V miditap-velo]
                  [-v] [-h]

DESCRIPTION

       aubioonset attempts to detect onset times, the beginning of discrete sound events, in audio signals.

       When  started  with  an  input source (-i/--input), the detected onset times are given on the console, in
       seconds.

       When started without an input source, or with the jack option  (-j/--jack),  aubioonset  starts  in  jack
       mode.

OPTIONS

This program follows the usual GNU command line syntax, with long options starting with two dashes (--).
A summary of options is included below.

-i, --input source
Run analysis on this audio file. Most uncompressed and compressed are supported, depending on how
aubio was built.

-o, --output sink
Save results in this file. The file will be created on the model of the input file. Onset times
are marked by a short wood-block like sound.

-r, --samplerate rate
Fetch the input source, resampled at the given sampling rate. The rate should be specified in
Hertz as an integer. If 0, the sampling rate of the original source will be used. Defaults to 0.

-B, --bufsize win
The size of the buffer to analyze, that is the length of the window used for spectral and temporal
computations. Defaults to 512.

-H, --hopsize hop
The number of samples between two consecutive analysis. Defaults to 256.

-O, --onset method
The onset detection method to use. See ONSET METHODS below. Defaults to 'default'.

-t, --onset-threshold thres
Set the threshold value for the onset peak picking. Values are typically in the range [0.001,
0.900]. Lower threshold values imply more onsets detected. Increasing this threshold should reduce
the number of incorrect detections. Defaults to 0.3.

-M, --minioi value
Set the minimum inter-onset interval, in seconds, the shortest interval between two consecutive
onsets. Defaults to 0.020

-s, --silence sil
Set the silence threshold, in dB, under which the onset will not be detected. A value of -20.0
would eliminate most onsets but the loudest ones. A value of -90.0 would select all onsets.
Defaults to -90.0.

-T, --timeformat format
Set time format (samples, ms, seconds). Defaults to seconds.

-m, --mix-input
Mix source signal to the output signal before writing to sink.

-f, --force-overwrite
Overwrite output file if it already exists.

-j, --jack
Use Jack input/output. You will need a Jack connection controller to feed aubio some signal and
listen to its output.

-N, --miditap-note
Override note value for MIDI tap. Defaults to 69.

-V, --miditap-velop
Override velocity value for MIDI tap. Defaults to 65.

-h, --help
Print a short help message and exit.

-v, --verbose
Be verbose.

ONSET METHODS

       Available methods are:

       default
              Default distance, currently hfc

       Default: 'default' (currently set to hfc)

       energy Energy based distance

       This function calculates the local energy of the input spectral frame.

       hfc    High-Frequency content

       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.

       complex
              Complex domain onset detection function

       This function uses information both in frequency and in  phase  to  determine  changes  in  the  spectral
       content  that  might  correspond  to  musical  onsets.   It  is  best  suited for complex signals such as
       polyphonic recordings.

       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.

       phase  Phase based onset detection function

       This  function  uses  information  both  in  frequency  and in phase to determine changes in the spectral
       content that might correspond to musical onsets. It is best suited for complex signals such as polyphonic
       recordings.

       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.

       specdiff
              Spectral difference 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.

       kl     Kulback-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.

       mkl    Modified Kulback-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.

       specflux
              Spectral flux

       Simon  Dixon,  Onset Detection Revisited, in ``Proceedings of the 9th International Conference on Digital
       Audio Effects'' (DAFx-06), Montreal, Canada, 2006.

AUTHOR