This is the command aubioonset that can be run in the OnWorks free hosting provider using one of our multiple free online workstations such as Ubuntu Online, Fedora Online, Windows online emulator or MAC OS online emulator
PROGRAM:
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]
[-s sil] [-m] [-f]
[-j] [-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. Typical values are typically
within 0.001 and 0.900. Defaults to 0.1. Lower threshold values imply more onsets
detected. Try 0.5 in case of over-detections. Defaults to 0.3.
-s, --silence sil
Set the silence threshold, in dB, under which the pitch 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.
-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.
-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.
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