Vamp Plugins

This page lists plugins made by research groups and developers around the world. It is generated automatically from RDF descriptions published by the plugin authors.

▶ Vamp Plugin Pack — Some of these plugins are also available in the Vamp Plugin Pack, a convenient bundle installer.

All plugins

BBC Vamp Plugins
BBC
Available for selected platform
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
A collection of audio feature extraction algorithms from BBC Research and Development.
▶ More
- Energy
  Low Level Features
  Calculates the RMS energy and low energy ratio of a signal.
- Intensity
  Low Level Features
  Calculates the intensity of a signal and the intensity ratio for a number of sub-bands.
- Peaks
  Low Level Features
  Calculates peak and trough values of a signal.
- Rhythm
  Low Level Features
  Calculates rhythmic features of a signal, including onsets and tempo.
- Spectral Contrast
  Low Level Features
  Calculates the peak and valleys of the spectral contrast feature.
- Spectral Flux
  Low Level Features
  Calculates the spectral flux.
- Speech/Music segmenter
  Classification
  Calculates boundaries between speech and music.
BeatRoot
Simon Dixon and Chris Cannam
Available for selected platform
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
A plugin implementation of the BeatRoot beat tracking system.
▶ More
- BeatRoot Beat Tracker
  Time → Tempo
  Identify beat locations in music.
Cepstral Pitch Tracker
Chris Cannam
Available for selected platform
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
A straightforward cepstral pitch- and note-tracker Vamp plugin, probably most suited to tracking singing pitch.
▶ More
- Cepstral Pitch Tracker
  Notes
  Estimate f0 of monophonic material using a cepstrum method.
Chordino and NNLS Chroma
Matthias Mauch
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
Harmony and chord extraction plugins by Matthias Mauch at C4DM.
▶ More
- Chordino
  Notes
  Chordino provides a simple chord transcription based on NNLS Chroma (as in the NNLS Chroma plugin). Chord profiles given by the user in the file chord.dict are used to calculate frame-wise chord similarities. Two simple (non-state-of-the-art!) algorithms are available that smooth these to provide a chord transcription: a simple chord change method, and a standard HMM/Viterbi approach.
- NNLS Chroma
  Visualisation
  This plugin provides a number of features derived from a DFT-based log-frequency amplitude spectrum: some variants of the log-frequency spectrum, including a semitone spectrum derived from approximate transcription using the NNLS algorithm; and based on this semitone spectrum, different chroma features.
- Tuning
  Key and Tonality
  The tuning plugin can estimate the local and global tuning of piece. The same tuning method is used for the NNLS Chroma and Chordino plugins.
Constant-Q
Queen Mary, University of London
Available for selected platform
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
A plugin implementing the Constant-Q transform of a time-domain signal.
▶ More
- Chromagram
  Visualisation
  Extract a Constant-Q spectrogram with constant ratio of centre frequency to resolution from the audio, then wrap it around into a single-octave chromagram.
- Constant-Q Spectrogram (Hz range)
  Visualisation
  Extract a spectrogram with constant ratio of centre frequency to resolution from the input audio, specifying the frequency range in Hz.
- Constant-Q Spectrogram (MIDI pitch range)
  Visualisation
  Extract a spectrogram with constant ratio of centre frequency to resolution from the input audio, specifying the frequency range in MIDI pitch units.
Expressive Means
Frithjof Vollmer and Chris Cannam
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
Plugins for analysis of articulation, vibrato, and portamento in audio recordings of musical performances, with a primary focus on bowed stringed instruments.
▶ More
- Expressive Means
  Time → Onsets
  Finds note onsets and durations in monophonic recordings based on changes in spectral content, power, and pitch.
- Expressive Means (advanced)
  Time → Onsets
  Finds note onsets and durations in monophonic recordings based on changes in spectral content, power, and pitch.
- Expressive Means (advanced): Articulation
  Notes → Expression
  Identifies types and intensities of articulation in monophonic recordings.
- Expressive Means (advanced): Portamento
  Notes → Expression
  Identifies types and intensities of portamento instances in monophonic recordings.
- Expressive Means: Articulation
  Notes → Expression
  Identifies types and intensity of articulation in monophonic recordings.
- Expressive Means: Portamento
  Notes → Expression
  Identifies types and intensities of portamento instances in monophonic recordings.
- Expressive Means (advanced): Pitch Vibrato
  Notes → Expression
  Identifies types and intensities of pitch vibrato instances in monophonic recordings.
- Expressive Means: Pitch Vibrato
  Notes → Expression
  Identifies types and intensities of pitch vibrato instances in monophonic recordings.
Fan Chirp F0gram
Audio Processing Group, Universidad de la Republica, Uruguay
Available for selected platform
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
Fundamental-frequency salience visualisation based on the Fan Chirp Transform, from the Universidad de la Republica, Uruguay.
▶ More
- Fan Chirp Transform F0gram
  Visualisation
  This plug-in produces a representation, called F0gram, which exhibits the salience of the fundamental frequency of the sound sources in the audio file. The computation of the F0gram makes use of the Fan Chirp Transform analysis. It is based on the article "Fan chirp transform for music representation" P. Cancela, E. Lopez, M. Rocamora, International Conference on Digital Audio Effects, 13th. DAFx-10. Graz, Austria - 6-10 Sep 2010.
HPCP - Harmonic Pitch Class Profile
Music Technology Group, Universitat Pompeu Fabra
Not available for selected platform
No source code
Plugin homepage
Download
Chroma feature estimation plugin.
▶ More
- HPCP
  Return the instantaneous evolution of HPCP (Harmonic Pitch Class Profile) of a signal.
INESC Porto Beat Tracker
SMC Group at INESC Porto
Available for selected platform
With source code
Plugin homepage
Download
Plugin from João Oliveira of the SMC Group for tempo induction and beat tracking, built on the MARSYAS framework.
▶ More
- IBT - INESC Beat Tracker
  Time → Tempo
  Estimates beat locations and tempo (off-line [default] and on-line modes of operation).
libxtract Vamp plugins
Chris Cannam and Jamie Bullock
Available for selected platform
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
Low-level feature extraction plugins using Jamie Bullock's libxtract library to provide around 50 spectral and other features.
▶ More
- Autocorrelation
  Low Level Features
  Extract the autocorrelation of an audio signal.
- Average Deviation
  Low Level Features
  Extract the average deviation of a range of values.
- Average Magnitude Difference Function
  Low Level Features
  Extract the AMDF of an audio signal.
- Average Squared Difference Function
  Low Level Features
  Extract the ASDF of an audio signal.
- Bark Coefficients
  Low Level Features
  Extract bark coefficients from an audio spectrum.
- Discrete Cosine Transform
  Visualisation
  Extract the DCT of an audio signal.
- Fundamental Frequency
  Low Level Features
  Extract the fundamental frequency of an audio signal.
- Fundamental Frequency (failsafe)
  Low Level Features
  Extract the fundamental frequency of an audio signal (failsafe).
- Harmonic Spectrum
  Visualisation
  Extract the harmonics from an audio spectrum.
- Highest Value
  Low Level Features
  Extract the highest value from a given range.
- Inharmonicity
  Low Level Features
  Extract the inharmonicity of an audio spectrum.
- Irregularity I
  Low Level Features
  Extract the irregularity (type I) of an audio spectrum.
- Irregularity II
  Low Level Features
  Extract the irregularity (type II) of an audio spectrum.
- Kurtosis
  Low Level Features
  Extract the kurtosis of a range of values.
- Loudness
  Low Level Features
  Extract the loudness of an audio signal from its spectrum.
- Lowest Value
  Low Level Features
  Extract the lowest value from a given range.
- Mean
  Low Level Features
  Extract the mean of a range of values.
- Mel-Frequency Cepstral Coefficients
  Low Level Features
  Extract MFCC from an audio spectrum.
- Noisiness
  Low Level Features
  Extract the noisiness of an audio spectrum.
- Non-zero count
  Low Level Features
  Extract the number of non-zero elements in an input spectrum.
- Odd/even Harmonic Ratio
  Low Level Features
  Extract the odd-to-even harmonic ratio of an audio spectrum.
- Peak Spectrum
  Visualisation
  Extract the spectral peaks from an audio spectrum.
- RMS Amplitude
  Low Level Features
  Extract the RMS amplitude of an audio signal.
- Skewness
  Low Level Features
  Extract the skewness of a range of values.
- Spectral Average Deviation
  Extract the average deviation of an audio spectrum.
- Spectral Centroid
  Low Level Features
  Extract the spectral centroid of an audio spectrum.
- Spectral Crest Measure
  Low Level Features
  Extract the spectral crest measure of an audio spectrum.
- Spectral Flatness
  Low Level Features
  Extract the spectral flatness of an audio spectrum.
- Spectral Kurtosis
  Low Level Features
  Extract the kurtosis of an audio spectrum.
- Spectral Rolloff
  Low Level Features
  Extract the rolloff point of an audio spectrum.
- Spectral Sharpness
  Low Level Features
  Extract the spectral sharpness of an audio spectrum.
- Spectral Skewness
  Low Level Features
  Extract the skewness of an audio spectrum.
- Spectral Slope
  Low Level Features
  Extract the spectral slope of an audio spectrum.
- Spectral Smoothness
  Low Level Features
  Extract the spectral smoothness of an audio spectrum.
- Spectral Spread
  Low Level Features
  Extract the spectral spread of an audio spectrum.
- Spectral Standard Deviation
  Low Level Features
  Extract the standard deviation of an audio spectrum.
- Spectral Variance
  Low Level Features
  Extract the variance of an audio spectrum.
- Spectrum
  Visualisation
  Extract the spectrum of an audio signal.
- Standard Deviation
  Low Level Features
  Extract the standard deviation of a range of values.
- Sum of Values
  Low Level Features
  Extract the sum of the values in a given range.
- Tonality
  Low Level Features
  Extract the tonality an audio spectrum.
- Tristimulus I
  Low Level Features
  Extract the tristimulus (type I) of an audio spectrum.
- Tristimulus II
  Low Level Features
  Extract the tristimulus (type II) of an audio spectrum.
- Tristimulus III
  Low Level Features
  Extract the tristimulus (type III) of an audio spectrum.
- Variance
  Low Level Features
  Extract the variance of a range of values.
- Zero Crossing Rate
  Low Level Features
  Extract the zero crossing rate of an audio signal.
MARSYAS Vamp plugins
Marsyas Plugins
With source code
Included in Vamp Plugin Pack
Plugin homepage
Low-level feature extraction plugins containing functionality from the MARSYAS batch feature extractor.
▶ More
- Marsyas - Batch Feature Extract - Centroid
  Low Level Features
  Marsyas - Batch Feature Extract - Centroid.
- Marsyas - Batch Feature Extract - Line Spectral Pairs
  Low Level Features
  Marsyas - Batch Feature Extract - Line Spectral Pairs.
- Marsyas - Batch Feature Extract - Linear Prediction Cepstral Coefficients
  Low Level Features
  Marsyas - Batch Feature Extract - Linear Prediction Cepstral Coefficients.
- Marsyas - Batch Feature Extract - Mel-Frequency Cepstral Coefficients
  Low Level Features
  Marsyas - Batch Feature Extract - Mel-Frequency Cepstral Coefficients.
- Marsyas - Batch Feature Extract - Spectral Crest Factor
  Low Level Features
  Marsyas - Batch Feature Extract - Spectral Crest Factor.
- Marsyas - Batch Feature Extract - Spectral Flatness Measure
  Low Level Features
  Marsyas - Batch Feature Extract - Spectral Flatness Measure.
- Marsyas - Batch Feature Extract - Spectral Rolloff
  Low Level Features
  Marsyas - Batch Feature Extract - Spectral Rolloff.
- Marsyas - Batch Feature Extract - Zero Crossings
  Low Level Features
  Marsyas - Batch Feature Extract - Zero Crossings.
MATCH Vamp Plugin
Simon Dixon and Chris Cannam
Available for selected platform
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
Vamp implementation of the MATCH audio alignment algorithm from Simon Dixon. Sonic Visualiser can use this for automatic time alignment among multiple audio files.
▶ More
- Match Performance Aligner
  Time → Alignment
  Calculate alignment between two performances in separate channel inputs.
Mazurka Plugins
The Mazurka Project
Not available for selected platform
No source code
Plugin homepage
Download
Spectral visualisation and feature extraction plugins from the Mazurka project.
▶ More
- Chronogram
  Visualisation
  Chronogram.
- Harmonic Spectrogram
  Visualisation
  Harmonic Spectrogram.
- Nevermore Spectrogram
  Visualisation
  Nevermore Spectrogram.
- Power Curve
  Low Level Features
  Power Curve.
- Spectral Flux
  Low Level Features
  Spectral Flux.
- Spectral Reflux
  Low Level Features
  Spectral Reflux.
MELODIA - Melody Extraction
Music Technology Group, Universitat Pompeu Fabra
Available for selected platform
No source code
Plugin homepage
Download
Melody pitch estimator for polyphonic music.
▶ More
- MELODIA - Melody Extraction
  Pitch
  Estimates the melody pitch in polyphonic music (also good for homophonic and monophonic music). Segments without melody are indicated by zero or negative values. For further details please read:
  J. Salamon and E. Gomez, "Melody Extraction from Polyphonic Music Signals using Pitch Contour Characteristics", IEEE Transactions on Audio, Speech and Language Processing, 20(6):1759-1770, 2012.
  We would highly appreciate the above reference being cited in publications of work in which this plug-in was used.
- MELODIA - Melody Extraction (intermediate steps)
  Visualisation
  Provides visualisations of the intermediate steps calculated by the melody extraction algorithm implemented in the MELODIA - Melody Extraction plug-in. For further details please read:
  J. Salamon and E. Gomez, "Melody Extraction from Polyphonic Music Signals using Pitch Contour Characteristics", IEEE Transactions on Audio, Speech and Language Processing, 20(6):1759-1770, 2012.
  We would highly appreciate the above reference being cited in publications of work in which this plug-in was used.
MIR.EDU
MIR.EDU by Justin Salamon
With source code
Plugin homepage
An open source Vamp plug-in library written in C++ which implements a basic set of descriptors useful for teaching MIR.
▶ More
- MIR.EDU: Attack Start/End Times
  Compute the start and end times of the attack of the signal. The start and end times of the attack are computed according to Peeters (2004), see code/documentation for further details. NOTE: the accuracy of the estimation depends on the step (hop) size of the analysis, the smaller the better. The times are returned as timestamps without any values.
- MIR.EDU: Log Attack Time
  Compute the logarithm (base 10) of the duration of the attack of the signal (unit: log10(seconds)). The start and end times of the attack are computed according to Peeters (2004), see code/documentation for further details. NOTE: the accuracy of the estimation depends on the step (hop) size of the analysis, the smaller the better. The the timestamp of the returned value is the start time of the attack. For visualising the attack start and end times use the 'Attack Start/End Times' plug-in.
- MIR.EDU: MFCC
  Compute the Mel Frequency Cepstral Coefficients (MFCC) for each frame. MFCCs provide a concise representation of the spectral envelope of a sound, which in turn is related to the sound's timbre. Please refer to the code in MFCC.cpp and the reference provided in MFCC.h for a detailed explanation of how MFCCs are computed.
- MIR.EDU: RMS
  Compute the root mean square of the signal for each frame.
- MIR.EDU: Spectral Centroid
  Compute the spectral centroid of the signal for each frame. The unit of the values returned is Hz. If the frame is completely silent a value of 0 is returned.
- MIR.EDU: Spectral Crest
  Compute the spectral crest of the signal for each frame. The crest is defined as the ratio between the maximum spectral magnitude in the frame and the arithmetical mean of the spectral magnitudes. The minimum possible value is 1 (flat spectrum) and it increases the peakier the spectrum is. If the frame is completely silent a value of 1 (flat spectrum) is returned.
- MIR.EDU: Spectral Flatness
  Compute the spectral flatness of the signal for each frame. The flatness is defined as the ratio of the geometric and arithmetical means of the spectral magnitudes. The values returned range between 0 (peaky spectrum) and 1 (flat spectrum). If the frame is completely silent a value of 1 (flat spectrum) is returned.
- MIR.EDU: Spectral Flux
  Compute the spectral flux between every two consecutive frames of the signal. The flux is defined as 1 minus the normalized correlation between successive magnitude spectra. The values returned range between 0 (no change) and 1 (maximum change). For the first frame the flux is always zero. If both frames are silent flux = 0, if only one is silent flux = 1.
- MIR.EDU: Spectral Kurtosis
  Compute the spectral kurtosis of the signal for each frame. The kurtosis is a measure of the peakedness of a distribution. For a gaussian distribution kurtosis = 3, for a flat distribution kurtosis < 3 and for a peakier distribution kurtosis > 3.If the frame is completely silent a value of 0 is returned.
- MIR.EDU: Spectral Roll-off
  Compute the spectral roll-off of the signal for each frame, defined as the frequency below which 95% of the signal energy is contained. The threshold (95%) is defined as a paramtere that can be changed by the user. The unit of the values returned is Hz. If the frame is completely silent a value of 0 is returned.
- MIR.EDU: Spectral Skewness
  Compute the spectral skewness of the signal for each frame. If the frame is completely silent a value of 0 is returned.
- MIR.EDU: Spectral Spread
  Compute the spectral spread of the signal for each frame. The unit of the values returned is Hz. If the frame is completely silent a value of 0 is returned.
- MIR.EDU: Temporal Centroid
  Compute the temporal centroid of the entire signal, which is the centre of gravity of the energy of the signal. Energy is represented by the RMS of the signal. The termporal centroid is computed between times n1 and n2 which are the first and last times the signal RMS is above 15% of its maximum value. The temporal centroid is returned as a timestamp with no corresponding value.
- MIR.EDU: Zero Crossing Rate
  Compute the zero crossing rate of the signal for each frame (i.e. the number of times the signal changes sign). The unit of the values returned is crossings/second.
OFA Vamp Plugin
Chris Cannam
With source code
Plugin homepage
Plugin that performed audio fingerprinting and lookup using the no-longer-supported MusicIP OFA library. This plugin is provided for interest only and is no longer practically useful.
▶ More
- MusicIP Audio Fingerprinter
  Calculates an audio fingerprint using the MusicIP OFA fingerprinting library.
- MusicIP PUID Lookup
  Calculates an audio fingerprint using the MusicIP OFA fingerprinting library and uses it to look up a MusicDNS PUID.
OnsetsDS plugin
Dan Stowell
With source code
Plugin homepage
Download
Note onset detector using Dan Stowell's OnsetsDS library.
▶ More
- OnsetsDS Onset Detector
  Time → Onsets
  Detect note onsets.
pYIN
Matthias Mauch
Available for selected platform
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
pYIN is a modification of the well-loved YIN algorithm for fundamental frequency (F0) estimation in monophonic audio.
▶ More
- Local Candidate PYIN
  Monophonic pitch and note tracking based on a probabilistic Yin extension.
- pYin
  Pitch
  Monophonic pitch and note tracking based on a probabilistic Yin extension.
- Yin
  Pitch
  A vamp implementation of the Yin algorithm for monophonic frequency estimation.
Queen Mary plugin set
Queen Mary, University of London
Available for selected platform
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
Plugins from the Centre for Digital Music at Queen Mary, University of London.
▶ More
- Adaptive Spectrogram
  Visualisation
  Produce an adaptive spectrogram by adaptive selection from spectrograms at multiple resolutions.
  Documentation
- Bar and Beat Tracker
  Time → Tempo
  Estimate bar and beat locations.
  Documentation
- Chromagram
  Visualisation
  Extract a series of tonal chroma vectors from the audio.
  Documentation
- Constant-Q Spectrogram
  Visualisation
  Extract a spectrogram with constant ratio of centre frequency to resolution from the input audio.
  Documentation
- Discrete Wavelet Transform
  Visualisation
  Visualisation by scalogram.
  Documentation
- Key Detector
  Key and Tonality
  Estimate the key of the music.
  Documentation
- Mel-Frequency Cepstral Coefficients
  Low Level Features
  Calculate a series of MFCC vectors from the audio.
  Documentation
- Note Onset Detector
  Time → Onsets
  Estimate individual note onset positions.
  Documentation
- Polyphonic Transcription
  Notes
  Transcribe the input audio to estimated notes.
  Documentation
- Segmenter
  Classification
  Divide the track into a sequence of consistent segments.
  Documentation
- Similarity
  Classification
  Return a distance matrix for similarity between the input audio channels.
  Documentation
- Tempo and Beat Tracker
  Time → Tempo
  Estimate beat locations and tempo.
  Documentation
- Tonal Change
  Key and Tonality
  Detect and return the positions of harmonic changes such as chord boundaries.
  Documentation
RGU Mel-Frequency Spectrum
Robert Gordon University
Available for selected platform
Requires VamPy
With source code
Plugin homepage
Download
A music-inspired texture representation implemented as a VamPy plugin, from Robert Gordon University in Aberdeen.
▶ More
- Vampy MFS Plugin
  MFS plugin.
Segmentino
Queen Mary, University of London
Available for selected platform
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
Plugin for automatic music structural segmentation.
▶ More
- Segmentino
  Classification
  Estimate contiguous segments pertaining to song parts such as verse and chorus.
Silvet Note Transcription
Queen Mary, University of London
Available for selected platform
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
Silvet, or Shift-Invariant Latent Variable Transcription, is a Vamp plugin for polyphonic music transcription (from audio to note times and pitches).
▶ More
- Silvet Note Transcription
  Notes
  Estimate the note onsets, pitches, and durations that make up a music recording.
Simple Cepstrum
Chris Cannam
With source code
Included in Vamp Plugin Pack
Plugin homepage
A simple Vamp plugin to calculate and return cepstrum values from DFT bins. Useful as a preliminary tool for looking at cepstral data for simple pitch or envelope methods.
▶ More
- Simple Cepstrum
  Low Level Features
  Return simple cepstral data from DFT bins. This plugin is intended for casual inspection of cepstral data. It returns a lot of different sorts of data and is quite slow; it's not a good way to extract a single feature rapidly.
Stereo Plan
Chris Cannam
With source code
Included in Vamp Plugin Pack
Plugin homepage
Azimuth-based stereo plan.
▶ More
- Stereo Plan
  Return a stereo plan decomposition of the audio. The returned feature grid covers the stereo plan from left-channel-only (first bin) to right-channel-only (last bin), with each value indicating what proportion of signal energy is found at that point on the plan at that moment. The input should consist of two channels containing left and right channel signals.
Tempogram
Carl Bussey
Available for selected platform
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
A Vamp plugin implementation of the tempogram and cyclic tempogram features described in Grosche, Müller, and Kurth 2010.
▶ More
- Tempogram
  Cyclic Tempogram as described by Peter Grosche and Meinard Müller.
Tipic - Tibre-Invariant Pitch Chroma
Queen Mary, University of London
Available for selected platform
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
Pitch-chroma audio features approaching timbre invariance, after the paper "Towards timbre-invariant audio features for harmony-based music" by Meinard Müller and Sebastian Ewert.
▶ More
- Timbre-Invariant Pitch Chroma
  Pitch and chroma features with optional DCT timbre reduction.
Tuning Difference
Chris Cannam
No source code
Included in Vamp Plugin Pack
Plugin homepage
Download
Estimate the tuning frequency of a recording, by comparing it to another recording of the same music whose tuning frequency is known.
▶ More
- Tuning Difference
  Estimate the tuning frequency of a recording, by comparing it to another recording of the same music whose tuning frequency is known.
University of Alicante Vamp Plugins
University of Alicante
Available for selected platform
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
The UAPlugins set is a library of Vamp plugins developed in the DRIMS project to perform onset detection and polyphonic transcription. The methods used in this library were developed by Antonio Pertusa and José Manuel Iñesta.
▶ More
- Note Onset Detector
  Time → Onsets
  Note onset detection using a one-semitone filterbank.
- Polyphonic Transcription
  Notes
  Multiple fundamental frequency estimation for polyphonic music transcription.
Vamp Aubio plugins
Paul Brossier
Available for selected platform
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
The Vamp Aubio Plugins provide methods from Paul Brossier's aubio annotation library, including beat and tempo tracking, onset detection, pitch detection, note tracking, silence detector, and Mel-frequency cepstral coefficients.
▶ More
- Aubio Beat Tracker
  Time → Tempo
  Estimate the musical tempo and track beat positions.
- Aubio Mel-Frequency Bands Extractor
  Low Level Features
  Compute energy in each 40 mel-frequency bands.
- Aubio Mfcc Extractor
  Low Level Features
  Extract Mel-Frequency Cepstrum Coefficients.
- Aubio Note Tracker
  Notes
  Estimate note onset positions, pitches and durations.
- Aubio Onset Detector
  Time → Onsets
  Estimate note onset times.
- Aubio Pitch Detector
  Pitch
  Track estimated note pitches.
- Aubio Silence Detector
  Low Level Features
  Detect levels below a certain threshold.
- Aubio Spectral Descriptor
  Low Level Features
  Compute spectral description function.
Vamp example plugins
Vamp SDK Example Plugins
Available for selected platform
With source code
Included in Vamp Plugin Pack
Plugin homepage
Download
A set of simple plugins as included with the Vamp developers kit. Amplitude tracker, simple percussion onset detector, tempo estimator, spectral centroid, power spectrum, and zero-crossing counter.
▶ More
- Amplitude Follower
  Low Level Features
  Track the amplitude of the audio signal.
  Documentation
- Simple Fixed Tempo Estimator
  Time → Tempo
  Study a short section of audio and estimate its tempo, assuming the tempo is constant.
  Documentation
- Simple Percussion Onset Detector
  Time → Onsets
  Detect percussive note onsets by identifying broadband energy rises.
  Documentation
- Simple Power Spectrum
  Visualisation
  Return the power spectrum of a signal.
  Documentation
- Spectral Centroid
  Low Level Features
  Calculate the centroid frequency of the spectrum of the input signal.
  Documentation
- Zero Crossings
  Low Level Features
  Detect and count zero crossing points.
  Documentation

How to Install

A Vamp plugin set consists of a single dynamic library file with .dll, .dylib, or .so extension (depending on your platform), plus optionally a category file with .cat extension and an RDF description file with .ttl or .n3 extension.

To install a plugin set, copy the plugin's library file and any supplied category or RDF files into your system or personal Vamp plugin location.

The plugin file extension and the location to copy into depend on which operating system you are using:

You can alternatively set the VAMP_PATH environment variable to override the search path for for Vamp plugins. VAMP_PATH should contain a semicolon-separated (on Windows) or colon-separated (macOS, Linux) list of directory locations. If it is set, it will completely override the standard locations listed above. (N.B. When using 32-bit plugins on 64-bit Windows, some hosts will check for the VAMP_PATH_32 environment variable instead of VAMP_PATH.)

Your operating system	File extension for plugins	Where to put the plugin files
macOS	`.dylib`	On a Mac: Put plugins for all users to use in `/Library/Audio/Plug-Ins/Vamp` Put plugins for only the current user in `$HOME/Library/Audio/Plug-Ins/Vamp` The `Library` folders are hidden by default; see here for details of how to show them
64-bit Windows	`.dll`	When using a 64-bit version of Windows: Put 32-bit plugins in `C:\Program Files (x86)\Vamp Plugins` Put 64-bit plugins in `C:\Program Files\Vamp Plugins` Both 32-bit and 64-bit plugins can be used, as long as you put them in the right places as above If a plugin package is not described as 64-bit, then it is a 32-bit plugin. Some older plugins were only published in 32-bit form.
32-bit Windows	`.dll`	When using a 32-bit version of Windows: Put 32-bit plugins in `C:\Program Files\Vamp Plugins` You cannot use 64-bit plugins at all on 32-bit Windows If a plugin package is not described as 64-bit, then it is a 32-bit plugin. Some older plugins were only published in 32-bit form.
Linux, other Unix	`.so`	On Linux, BSD systems, etc: Put plugins for all users to use in `/usr/local/lib/vamp` Put plugins for only the current user in `$HOME/vamp` Only plugins with the correct architecture can be used (32-bit plugins on 32-bit systems, and 64-bit on 64-bit).

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