license: apache-2.0
language:
- en
tags:
- audio
- timbre
- guitar
pretty_name: Semantic Timbre Dataset
Dataset Card for the Semantic Timbre Dataset
The Semantic Timbre Dataset is a dataset of electric guitar audio samples labelled with 19 timbre descriptors at gradually increasing timbre magnitude.
Dataset Details
The Semantic Timbre Dataset contains 275,310 audio files of monophonic electric guitar sounds. These 275,310 audio files are grouped into 19 semantic timbre descriptors which describe the timbral characteristics of each sound. The 19 timbre descriptors are:
- Crunchy
- Crushed
- Dirty
- Fuzzy
- Bright
- Dark
- Fat
- Resonant
- Thin
- Punchy
- Sharp
- Soft
- Smooth
- Tight
- Fluttery
- Jittery
- Shimmering
- Stuttering
- WahWah
These timbre descriptors were derived from the most popularly-occurring timbre descriptor keywords appearing on the parameters of physical and virtual guitar effects units.
Each timbre descriptor group contains 14,490 audio files that have been produced by pedal parameter effects from Native Instrument's Guitar Rig Pro 7 that correspond to the timbre descriptor. Each pedal parameter has been adjusted in increments of 5 from 0 to 100, resulting in each timbre descriptor group of 14,490 audio files being split further into 21 groups of 690 audio files that increase in timbre magnitude by increments of 5 from 0 to 100.
The Semantic Timbre Dataset is a collection of audio .wav files located within labelled subfolders with the following pattern:
SemanticTimbreDataset/audio/TIMBRE_GROUP/TIMBRE_DESCRIPTOR/TIMBRE_MAGNITUDE
where TIMBRE_GROUP is one of: 'DistortionFX', 'FilterFX', 'DynamicsFX', or 'OscillationFX',
and TIMBRE_GROUP/TIMBRE_DESCRIPTOR is one of: - DistortionFX / 'Crunch', 'Crush', 'Dirt', or 'Fuzz'
- FilterFX / 'Bright', 'Dark', 'Fat', 'Thin', or 'Resonant'
- DynamicsFX / 'Punch', 'Sharp', 'Smooth', 'Soft', or 'Tight'
- OscillationFX / 'Fluttery', 'Jittery', 'Shimmery', 'Stuttering', or 'WahWah',
and TIMBRE_MAGNITUDE is one of: '0', '5', '10', '15', '20', '25', '30', '35', '40', '45', '50', '55', '60', '65', '70', '75', '80', '85', '90', '95', or '100'.
The name of each individual .wav audio file within the sub-folders of the Semantic Timbre Dataset corresponds to both the pitch of the note within the audio file and the pickup configuration of the original Fender Stratocaster electric guitar that produced the original recording from the EGFxSet created by Hegel Pedroza, Irán Roman, and Gerardo Meza. The filename pattern is dictated like so:
PITCH_PICKUPCONFIGURATION.wav
where PITCH is denoted by the String-Fret tuple. The String-Fret tuple is two digits indicating the string number (1 through 6, where 1 is the highest-pitch string and 6 is the lowest-pitch string) and fret number (0 for open strings, and 1 through 22 for fret position). An example is '1-0',
and PICKUPCONFIGURATION is one of the five possible pickup configurations: 'Bridge', 'Bridge-Middle', 'Middle', 'Middle-Neck', or 'Neck'.
'Bridge' is denoted by nothing, 'Bridge-Middle' is denoted by '_1', 'Middle' is denoted by '_2', 'Middle-Neck' is denoted by '_3', and 'Neck' is denoted by '_4'.
Hence, the filename '1-0_2.wav' within the Semantic Timbre Dataset represents an E4 note on a Fender Stratocaster electric guitar (the first/highest open string) with the middle pickup configuration. The filename '1-5.wav' within the Semantic Timbre Dataset represents an A4 note on a Fender Stratocaster electric guitar (the first/highest string on the 5th fret) with the bridge pickup configuration.
Direct Use
The Semantic Timbre Dataset can be used to train generative AI models to generate electric guitar sounds to specific timbral specification that can be described by the dataset's 19 semantic timbre descriptors.
Source Data
The original, clean, unprocessed 690 audio recordings of Fender Stratocaster monophonic notes originally come from the EGFxSet created by Hegel Pedroza, Irán Roman, and Gerardo Meza.
Who are the annotators?
Personal and Sensitive Information
The Semantic Timbre Dataset contains no personal or sensitive information.