This project contains Python scripts developed for extracting timbral attributes of audio files.
More detailed explanations of how the models function can be found in Deliverable D5.2: First prototype of timbral characterisation tools for semantically annotating non-musical content, available: http://www.audiocommons.org/materials/
The timbral_models package can be installed using the pip command. This will handle all dependencies.
pip install timbral_models
Note that during test installing the package with only basic python installed, an error occurred when installing dependencies. This can be overcome by first installing numpy, followed by timbral_models.
pip install numpy
pip install timbral_models
If the code is to be installed using a method other than pip, dependencies will need to be installed. The timbral models rely on several other easily accessible python packages: numpy, soundfile, librosa, and scipy. These are all easily installed using the pip install command. e.g.
$ pip install numpy
$ pip install soundfile
$ pip install librosa
$ pip install scipy
The models are written as a package which can be imported into a Python script. Within the package are six methods that predict the brightness, depth, hardness, metallic-nature, reverb, and roughness of an audio file. These are named timbral_xxx(fname), where xxx represents the timbral model.
To calculate the timbral attribute, give the method a string of the file name. The method will then read in the audio file internally and return the timbral characteristic, as described below.
The hardness, depth, and brightness models predict subjective ratings of their respective attributes. Each model returns a float. These models were trained on subjective ratings ranging from 0 to 100, but can extend beyond this range.
The metalic-nature model returns the probability of an audio file sounding metallic as a float ranging from 0.0 to 1.0.
The roughness model returns a float representing the roughness of the audio file. The minimum roughness value is 0.0, but there is no upper limit on the maximum roughness value.
The reverb model returns an approximation of the RT60 of an audio file in milliseconds. If the algorithm cannot estimate an RT60, 0 is returned.
from timbral_models import timbral_brightness
# generic file location
fname = '/Users/User/Music/AudioFileToTest.wav'
# calculate brightness
brightness = timbral_brightness(fname)
This section documents the version history of the timbral models. To download a specific version of the model that relate to a specific deliverable, please check this section and download the most recent version from that date.
2017/09/05 - Version 0.1 of timbral models, relates to Audio Commons Deliverable D5.3. This version of the repository relates to the software version 0.1 on PyPI.
2017/04/27 - Version 0.0 of the timbral models, relates to Audio Commons Deliverable D5.2.