SAWA: The OMRAS2 Music Analysis and Feature Extraction Service

Overview

SAWA demonstrates audio feature extraction technology developed at the Center for Digital Music, our standard plugin API for audio analysis and a Semantic Web ontology based representation for returning the results of the analysis. It is primarily aimed at researchers who wish to become familiar with these technologies. SAWA may be used as a batch feature extractor, for example, for automatically generating a small reference data set in RDF format. The resulting files can be loaded in a suitable RDF database and queried over, they may be browsed using a Semantic Web browser (e.g. the OpenLink RDF browser) or loaded and visualised with Sonic Visualiser a tool for studying the content of audio files. [For the more Semantic Web savvy: a suitable machine interface (SPARQL endpoint) for retrieving past result automatically is currently under testing but it is not yet available in this public release...]

Using SAWA

You can upload one or more audio files and select one or more of the available Vamp plugin transforms. (At least one file and one selected transform is required.) The service accepts a number of file formats including .wav and .mp3. The results are returned for each file in RDF or some human readable rendering. Please follow the processing steps outlined below. Note that some steps are optional.

	Processing steps:

	1. Upload one or more music files
	2. Uploaded files are optionally identified by using MusicDNS OFA acoustic fingerprint and MusicBrainz.
	3. Choose one or more feature extractors (Vamp plugin transforms)
	4. Configure feature extractors (optional)
	5. Run the feature extractors by pressing the submit button at the bottom of the page
	6. Wait for the computation to complete
	7. View or download the results in Music Ontology RDF, use an RDF browser such as Tabulator, or load with Sonic Visualiser

File upload

Please upload an audio file:   

Identify the file and cache resulting features (default).
This is just a test. Please do not identify the file or cache any features.

According to our copyright policy,uploaded files will be deleted as the session expires.

Available plugins

Please select at least one VAMP plugin transform.
You can switch between compact or detailed view of plugin outputs using: toggle outputs.

Vampy - Python Vamp Plugins

TempEst Temperament Estimator  Select Output Description Select Transform Temperament Estimate The output of the temperament classifier default transform configure Tuning frequency estimate (Kammerton) An estimate of the reference tuning frequency default transform configure Ultra Conservative Transcription Estimate some note frequencies with high confidence default transform configure

Vamp Example Plugins

Amplitude Follower Track the amplitude of the audio signal  Select Output Description Select Transform Amplitude The amplitude curve default transform configure Simple Percussion Onset Detector Detect percussive note onsets by identifying broadband energy rises  Select Output Description Select Transform Detection Function Broadband energy rise detection function default transform configure Onsets Percussive note onset locations default transform configure Simple Fixed Tempo Estimator Study a short section of audio and estimate its tempo, assuming the tempo is constant  Select Output Description Select Transform Detection Function Onset detection function default transform configure Tempo Estimated tempo default transform configure Tempo candidates Possible tempo estimates, one per bin with the most likely in the first bin default transform configure Autocorrelation Function Autocorrelation of onset detection function default transform configure Filtered Autocorrelation Filtered autocorrelation of onset detection function default transform configure Zero Crossings Detect and count zero crossing points  Select Output Description Select Transform Zero Crossings The locations of zero crossing points default transform configure Zero Crossing Counts The number of zero crossing points per processing block default transform configure Simple Power Spectrum Return the power spectrum of a signal  Select Output Description Select Transform Power Spectrum Power values of the frequency spectrum bins calculated from the input signal default transform configure Spectral Centroid Calculate the centroid frequency of the spectrum of the input signal  Select Output Description Select Transform Log Frequency Centroid Centroid of the log weighted frequency spectrum default transform configure Linear Frequency Centroid Centroid of the linear frequency spectrum default transform configure

QM Vamp Plugins

Tempo and Beat Tracker Estimate beat locations and tempo  Select Output Description Select Transform Onset Detection Function Probability function of note onset likelihood default transform configure Beats Estimated metrical beat locations default transform configure Tempo Locked tempo estimates default transform configure Polyphonic Transcription Transcribe the input audio to estimated notes  Select Output Description Select Transform Transcription Estimated note pitch (MIDI note number from 0 to 127) default transform configure Constant-Q Spectrogram Extract a spectrogram with constant ratio of centre frequency to resolution from the input audio  Select Output Description Select Transform Constant-Q Spectrogram Output of constant-Q transform, as a single vector per process block default transform configure Chromagram Extract a series of tonal chroma vectors from the audio  Select Output Description Select Transform Chromagram Output of chromagram, as a single vector per process block default transform configure Chroma Means Mean values of chromagram bins across the duration of the input audio default transform configure Discrete Wavelet Transform Visualisation by scalogram  Select Output Description Select Transform Wavelet Coefficients Wavelet coefficients default transform configure Mel-Frequency Cepstral Coefficients Calculate a series of MFCC vectors from the audio  Select Output Description Select Transform Coefficients MFCC values default transform configure Means of Coefficients Mean values of MFCCs across duration of audio input default transform configure Key Detector Estimate the key of the music  Select Output Description Select Transform Tonic Pitch Tonic of the estimated key (from C = 1 to B = 12) default transform configure Key Mode Major or minor mode of the estimated key (major = 0, minor = 1) default transform configure Key Estimated key (from C major = 1 to B major = 12 and C minor = 13 to B minor = 24) default transform configure Key Strength Plot Correlation of the chroma vector with stored key profile for each major and minor key default transform configure Note Onset Detector Estimate individual note onset positions  Select Output Description Select Transform Smoothed Detection Function Smoothed probability function used for peak-picking default transform configure Note Onsets Perceived note onset positions default transform configure Onset Detection Function Probability function of note onset likelihood default transform configure Tonal Change Detect and return the positions of harmonic changes such as chord boundaries  Select Output Description Select Transform Tonal Change Detection Function default transform configure Transform to 6D Tonal Content Space default transform configure Tonal Change Positions default transform configure Segmenter Divide the track into a sequence of consistent segments  Select Output Description Select Transform Segmentation Segmentation default transform configure Adaptive Spectrogram Produce an adaptive spectrogram by adaptive selection from spectrograms at multiple resolutions  Select Output Description Select Transform Output The output of the plugin default transform configure Similarity Return a distance matrix for similarity between the input audio channels  Select Output Description Select Transform Distance Matrix Distance matrix for similarity metric. Smaller = more similar. Should be symmetrical. default transform configure Distance from First Channel Distance vector for similarity of each channel to the first channel. Smaller = more similar. default transform configure Feature Means Means of the feature bins. Feature time (sec) corresponds to input channel. Number of bins depends on selected feature type. default transform configure Feature Variances Variances of the feature bins. Feature time (sec) corresponds to input channel. Number of bins depends on selected feature type. default transform configure Beat Spectra Rhythmic self-similarity vectors (beat spectra) for the input channels. Feature time (sec) corresponds to input channel. Not returned if rhythm weighting is zero. default transform configure Ordered Distances from First Channel Vector of the order of other channels in similarity to the first, followed by distance vector for similarity of each to the first. Smaller = more similar. default transform configure Bar and Beat Tracker Estimate bar and beat locations  Select Output Description Select Transform Bars Bar locations default transform configure Beat Count Beat counter function default transform configure Beats Beat locations labelled with metrical position default transform configure Beat Spectral Difference Beat spectral difference function used for bar-line detection default transform configure