cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7551605
title: IDMT-ISA-Compressed-Air Dataset

description: The IDMT-ISA-Compressed-Air (IICA) dataset aims to foster research in compressed air leak detection with acoustic emissions in the audible hearing range with recordings of air leaks in a simulated industrial compressed air network. The dataset contains recordings of multiple leak types with different types of industrial background noises played via external loudspeakers at two different volumes during the recording process. 


Leak Types:



	
Vent Leak
	
Vent Leak Low Pressure
	
Tube Leak



Noise Types:



	
Lab Noise (no added background noise)
	
Hydraulic machine noise
	
Hydraulic machine noise, low volume
	
General factory workshop noise
	
General factory workshop noise, low volume



For each combination of leak and noise types, there were three recording sessions. During each session, four Earthworks M30 omnidirectional measurement microphones placed in different configurations recorded the acoustic emission of the compressed air network. Each recording session contains 128 files of 30 seconds each, corresponding to each combination of leak, noise and microphone.



	
Total Files: 5592
	
Sampling Rate: 48 kHz
	
Resolution: 32-bit
	
Mono Audio



See the above referenced paper and README contained with the data folder for further details.
DOI: 10.5281/zenodo.7551606
title: IDMT-ISA-Compressed-Air Dataset

description: The IDMT-ISA-Compressed-Air (IICA) dataset aims to foster research in compressed air leak detection with acoustic emissions in the audible hearing range with recordings of air leaks in a simulated industrial compressed air network. The dataset contains recordings of multiple leak types with different types of industrial background noises played via external loudspeakers at two different volumes during the recording process. 


Leak Types:



	
Vent Leak
	
Vent Leak Low Pressure
	
Tube Leak



Noise Types:



	
Lab Noise (no added background noise)
	
Hydraulic machine noise
	
Hydraulic machine noise, low volume
	
General factory workshop noise
	
General factory workshop noise, low volume



For each combination of leak and noise types, there were three recording sessions. During each session, four Earthworks M30 omnidirectional measurement microphones placed in different configurations recorded the acoustic emission of the compressed air network. Each recording session contains 128 files of 30 seconds each, corresponding to each combination of leak, noise and microphone.



	
Total Files: 5592
	
Sampling Rate: 48 kHz
	
Resolution: 32-bit
	
Mono Audio



See the above referenced paper and README contained with the data folder for further details.
DOI: 10.5281/zenodo.7544031
title: IDMT-SMT-Audio-Effects Dataset

description: The IDMT-SMT-Audio-Effects database is a large database for automatic detection of audio effects in recordings of electric guitar and bass and related signal processing.


The overall duration of the audio material is approx. 30 hours.


The dataset consists of 55044 WAV files (44.1 kHz, 16bit, mono) with single recorded notes:



	
20592 monophonic bass notes
	
20592 monophonic guitar notes
	
13860 polyphonic guitar sounds



Overall, 11 different audio effects are incorporated:



	
feedback delay
	
slapback delay
	
reverb
	
chorus
	
flanger
	
phaser
	
tremolo
	
vibrato
	
distortion
	
overdrive
	
no effect (unprocessed notes/sounds)



2 different electric guitars and 2 different electric bass guitars, each with two different pick-up settings and up to three different plucking styles (finger plucked - hard, finger plucked - soft, picked) were used for recording.


The notes cover the common pitch range of a 4-string bass guitar from E1 (41.2 Hz) to G3 (196.0 Hz) or the common pitch range of a 6-string electric guitar from E2 (82.4 Hz) to E5 (659.3 Hz).


Effects processing was performed using a digital audio workstation and a variety of mostly freely available effect plugins.


To organize the database, lists in XML format are used, which record all relevant information and are provided with the database as well as a summary of the used effect plugins and parameter settings.


In addition, most of this information is also encoded in the first part of the file name of the audio files using a simple alpha-numeric encoding scheme. The second part of the file name contains unique identification numbers. This provides an option for fast and flexible structuring of the data for various purposes.
DOI: 10.5281/zenodo.7544032
title: IDMT-SMT-Audio-Effects Dataset

description: The IDMT-SMT-Audio-Effects database is a large database for automatic detection of audio effects in recordings of electric guitar and bass and related signal processing.


The overall duration of the audio material is approx. 30 hours.


The dataset consists of 55044 WAV files (44.1 kHz, 16bit, mono) with single recorded notes:



	
20592 monophonic bass notes
	
20592 monophonic guitar notes
	
13860 polyphonic guitar sounds



Overall, 11 different audio effects are incorporated:



	
feedback delay
	
slapback delay
	
reverb
	
chorus
	
flanger
	
phaser
	
tremolo
	
vibrato
	
distortion
	
overdrive
	
no effect (unprocessed notes/sounds)



2 different electric guitars and 2 different electric bass guitars, each with two different pick-up settings and up to three different plucking styles (finger plucked - hard, finger plucked - soft, picked) were used for recording.


The notes cover the common pitch range of a 4-string bass guitar from E1 (41.2 Hz) to G3 (196.0 Hz) or the common pitch range of a 6-string electric guitar from E2 (82.4 Hz) to E5 (659.3 Hz).


Effects processing was performed using a digital audio workstation and a variety of mostly freely available effect plugins.


To organize the database, lists in XML format are used, which record all relevant information and are provided with the database as well as a summary of the used effect plugins and parameter settings.


In addition, most of this information is also encoded in the first part of the file name of the audio files using a simple alpha-numeric encoding scheme. The second part of the file name contains unique identification numbers. This provides an option for fast and flexible structuring of the data for various purposes.
DOI: 10.5281/zenodo.7544098
title: IDMT-SMT-Bass-Single-Track Dataset

description: The IDMT-SMT-BASS-SINGLE-TRACK dataset comprises of 17 bass lines from different music styles.


It is intended as a public evaluation dataset for:



	
retrieval of repetitive bass patterns



For each bass line, the pattern length (in seconds) and the begin of the first pattern appearance (in seconds) is annotated.


The patterns are in general no exact repetitions but instead contain occasional
pitch and rhythm variations.



	
bass transcription



Each note is annotated with the score-related parameters onset, offset and pitch.



	
spatial transcription / estimation of the fretboard position




Each note is annotated with the instrument-related parameters string number and fret number.



	
estimation of bass guitar plucking styles



Each note is played and annotated with one of the 5 plucking style classes:



	
Finger-style (FS) - alternate plucking of the string using the index and
	middle finger
	
Picked (PK) - plucking of the string using a plastic pick
	
Muted (MU) - plucking of the string using the thumb and index finger while simultaneously damping the string vibration using the palm of the hand
	
Slap-Pluck (SP) - picking of a string using either the index or the middle finger (causing a collision between the string and the upper frets)
	
Slap-Thumb (ST)- striking of the string using the thumb (causing a collision between the string and the upper frets)



 



	
estimation of bass guitar expression styles (6 classes)



Each note is played and annotated with one of the 6 expression style classes:



	
Normal (NO) - no expression, just “regular” bass note playing
	
Harmonics (HA) - flageolet tones
	
Dead-note (DN) - damped, percussive note
	
Bending (BE) - singular bending of the string during vibration
	
Vibrato (VI) - periodic bending and releasing the string during vibration
	
Slide (SL) - slide up or down after note is plucked
DOI: 10.5281/zenodo.7544099
title: IDMT-SMT-Bass-Single-Track Dataset

description: The IDMT-SMT-BASS-SINGLE-TRACK dataset comprises of 17 bass lines from different music styles.


It is intended as a public evaluation dataset for:



	
retrieval of repetitive bass patterns



For each bass line, the pattern length (in seconds) and the begin of the first pattern appearance (in seconds) is annotated.


The patterns are in general no exact repetitions but instead contain occasional
pitch and rhythm variations.



	
bass transcription



Each note is annotated with the score-related parameters onset, offset and pitch.



	
spatial transcription / estimation of the fretboard position




Each note is annotated with the instrument-related parameters string number and fret number.



	
estimation of bass guitar plucking styles



Each note is played and annotated with one of the 5 plucking style classes:



	
Finger-style (FS) - alternate plucking of the string using the index and
	middle finger
	
Picked (PK) - plucking of the string using a plastic pick
	
Muted (MU) - plucking of the string using the thumb and index finger while simultaneously damping the string vibration using the palm of the hand
	
Slap-Pluck (SP) - picking of a string using either the index or the middle finger (causing a collision between the string and the upper frets)
	
Slap-Thumb (ST)- striking of the string using the thumb (causing a collision between the string and the upper frets)



 



	
estimation of bass guitar expression styles (6 classes)



Each note is played and annotated with one of the 6 expression style classes:



	
Normal (NO) - no expression, just “regular” bass note playing
	
Harmonics (HA) - flageolet tones
	
Dead-note (DN) - damped, percussive note
	
Bending (BE) - singular bending of the string during vibration
	
Vibrato (VI) - periodic bending and releasing the string during vibration
	
Slide (SL) - slide up or down after note is plucked
DOI: 10.5281/zenodo.7188891
title: IDMT-SMT-Bass Dataset

description: The IDMT-SMT-Bass database is a large database for automatic bass transcription and signal processing.


The overall duration of the audio material is approx. 3.6 hours.


The dataset consists of approx. 4300 WAV files (44.1 kHz, 24bit) with single recorded notes.


Overall, 10 different bass-related playing techniques namely 5 plucking styles



	
fingerstyle (FS)
	
picked (PK)
	
muted (MU)
	
slap-thumb (ST)
	
slap-pluck (SP)



and 5 expression styles



	
normal (NO)
	
vibrato (VI)
	
bending (BE)
	
harmonics (HA)
	
dead-note (DN)



are incorporated. A further explaination of the playing techniques is provided in




Abeßer, Jakob; Lukashevich, Hanna; Schuller, Gerald: Feature-based extraction of plucking and expression styles of the electric bass guitar, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2010



For each of the three expression techniques (vibrato, bending, slide), two subclasses were defined in:




Abesser, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Recognition and Parametrization of Frequency Modulation Techniques in Bass-Guitar Recordings, Proceedings of the 42nd AES Conference on Semantic Audio 2011, Ilmenau, Germany




	
fast vibrato, slow vibrato
	
semi-tone bending, quarter-note bending
	
slide up, slide down (recorded with fretless bass guitar)



3 different 4-string electric bass guitars, each with 3 different pick-up settings were used for recording.
 


The notes cover the common pitch range of a 4-string bass guitar from E1 (41.2 Hz) to G3 (196.0 Hz).
DOI: 10.5281/zenodo.7188892
title: IDMT-SMT-Bass Dataset

description: The IDMT-SMT-Bass database is a large database for automatic bass transcription and signal processing.


The overall duration of the audio material is approx. 3.6 hours.


The dataset consists of approx. 4300 WAV files (44.1 kHz, 24bit) with single recorded notes.


Overall, 10 different bass-related playing techniques namely 5 plucking styles



	
fingerstyle (FS)
	
picked (PK)
	
muted (MU)
	
slap-thumb (ST)
	
slap-pluck (SP)



and 5 expression styles



	
normal (NO)
	
vibrato (VI)
	
bending (BE)
	
harmonics (HA)
	
dead-note (DN)



are incorporated. A further explaination of the playing techniques is provided in




Abeßer, Jakob; Lukashevich, Hanna; Schuller, Gerald: Feature-based extraction of plucking and expression styles of the electric bass guitar, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2010



For each of the three expression techniques (vibrato, bending, slide), two subclasses were defined in:




Abesser, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Recognition and Parametrization of Frequency Modulation Techniques in Bass-Guitar Recordings, Proceedings of the 42nd AES Conference on Semantic Audio 2011, Ilmenau, Germany




	
fast vibrato, slow vibrato
	
semi-tone bending, quarter-note bending
	
slide up, slide down (recorded with fretless bass guitar)



3 different 4-string electric bass guitars, each with 3 different pick-up settings were used for recording.
 


The notes cover the common pitch range of a 4-string bass guitar from E1 (41.2 Hz) to G3 (196.0 Hz).
DOI: 10.5281/zenodo.7544163
title: IDMT-SMT-Drums Dataset

description: The IDMT-SMT-Drums database is a medium-sized database for automatic drum transcription and source separation.


The dataset consists of 608 WAV files (44.1 kHz, Mono, 16bit). The approximate duration is 2:10 hours.


There are 104 polyphonic drum set recordings (drum loops) containing only the drum instruments kick drum, snare drum and hi-hat. For each drum loop, there are 3 training files for the involved instruments, yielding 312 training files for drum transcription purposes. The recordings are from three different sources:



	
Real-world, acoustic drum sets (RealDrum)
	
Drum sample libraries (WaveDrum)
	
Drum synthesizers (TechnoDrum)



For each drum loop, the onsets of kick drum, snare drum and hi-hat have been manually annotated. They are provided as XML and SVL files that can be assigned to the corresponding audio recording by their filename. Appropriate annotation file parsers are provided as MATLAB functions together with an example script showing how to import the complete dataset.


The subsets TechnoDrum02 and WaveDrum02 contain 64 drum loops that are delivered together with perfectly isolated single tracks of kick drum, snare drum, and hi-hat in addition to the above-mentioned training files. Mixing the single tracks together yields the mixture drum loops, thus providing 192 reference signals for source separation experiments.
DOI: 10.5281/zenodo.7544164
title: IDMT-SMT-Drums Dataset

description: The IDMT-SMT-Drums database is a medium-sized database for automatic drum transcription and source separation.


The dataset consists of 608 WAV files (44.1 kHz, Mono, 16bit). The approximate duration is 2:10 hours.


There are 104 polyphonic drum set recordings (drum loops) containing only the drum instruments kick drum, snare drum and hi-hat. For each drum loop, there are 3 training files for the involved instruments, yielding 312 training files for drum transcription purposes. The recordings are from three different sources:



	
Real-world, acoustic drum sets (RealDrum)
	
Drum sample libraries (WaveDrum)
	
Drum synthesizers (TechnoDrum)



For each drum loop, the onsets of kick drum, snare drum and hi-hat have been manually annotated. They are provided as XML and SVL files that can be assigned to the corresponding audio recording by their filename. Appropriate annotation file parsers are provided as MATLAB functions together with an example script showing how to import the complete dataset.


The subsets TechnoDrum02 and WaveDrum02 contain 64 drum loops that are delivered together with perfectly isolated single tracks of kick drum, snare drum, and hi-hat in addition to the above-mentioned training files. Mixing the single tracks together yields the mixture drum loops, thus providing 192 reference signals for source separation experiments.
DOI: 10.5281/zenodo.7544212
title: IDMT-SMT-Chords Dataset

description: The IDMT-SMT-CHORDS comprises of 16 MIDI generated audio files consists of various chord classes. Here we focused on chord voicings, which are commonly used on keyboard instruments and guitars. Based on this we categorized as Guitar and Non-Guitar instruments. We used several software instruments from Ableton Live and Garage Band to synthesize these MIDI files with various instruments such as piano, synthesizer pad, as well as acoustic and electric guitar.



	
File duration: 4.1 Hours
	
# Chord segments: 7398
	
# WAV files: 16
	
Chord duration: 2 seconds
	
BPM: 120
	
Time signature: 4/4
	
Sampling rate: 44.1KHz
	
Mono audio



Non-Guitar


The Non-Guitar files includes all chord types in all possible root note positions and inversions. For example, C Major triad chord is included with its two possible inversions C/E and C/G.


All non-guitar chord classes are listed below:



	
Major (+ 2 inversions)
	
Minor (+ 2 inversions)
	
Major 7 (+ 3 inversions)
	
Minor 7 (+ 3 inversions)
	
Power Chord  - root and fifth note (+ 1 inversion)
	
Dominant 7 (+ 3 inversions)
	
Minor 7 flat 5 (+ 3 inversions)



This gives us 576 non-guitar chord classes.


Guitar


The guitar files where generated based on barŕe chord voicings with the root note located on the low E, A, and D strings. For example, to modeling major chord and it’s voicings we use open position E maj, A maj and  D maj shape and move 12 steps (including octave at 12th fret) thereby we get 39 positions (13*3).


List of  Guitar chord types:



	
Major (+ 2 voicings)
	
Minor (+ 2 voicings)
	
Major 7 (+ 2 voicings)
	
Minor 7 (+ 2 voicings)
	
Power Chord  - root and fifth note (+ 2 voicings)
	
Dominant 7 (+ 2 voicings)
	
Minor 7 flat 5 (+ 2 voicings)



This gives us 273 different guitar chord classes.
DOI: 10.5281/zenodo.7544213
title: IDMT-SMT-Chords Dataset

description: The IDMT-SMT-CHORDS comprises of 16 MIDI generated audio files consists of various chord classes. Here we focused on chord voicings, which are commonly used on keyboard instruments and guitars. Based on this we categorized as Guitar and Non-Guitar instruments. We used several software instruments from Ableton Live and Garage Band to synthesize these MIDI files with various instruments such as piano, synthesizer pad, as well as acoustic and electric guitar.



	
File duration: 4.1 Hours
	
# Chord segments: 7398
	
# WAV files: 16
	
Chord duration: 2 seconds
	
BPM: 120
	
Time signature: 4/4
	
Sampling rate: 44.1KHz
	
Mono audio



Non-Guitar


The Non-Guitar files includes all chord types in all possible root note positions and inversions. For example, C Major triad chord is included with its two possible inversions C/E and C/G.


All non-guitar chord classes are listed below:



	
Major (+ 2 inversions)
	
Minor (+ 2 inversions)
	
Major 7 (+ 3 inversions)
	
Minor 7 (+ 3 inversions)
	
Power Chord  - root and fifth note (+ 1 inversion)
	
Dominant 7 (+ 3 inversions)
	
Minor 7 flat 5 (+ 3 inversions)



This gives us 576 non-guitar chord classes.


Guitar


The guitar files where generated based on barŕe chord voicings with the root note located on the low E, A, and D strings. For example, to modeling major chord and it’s voicings we use open position E maj, A maj and  D maj shape and move 12 steps (including octave at 12th fret) thereby we get 39 positions (13*3).


List of  Guitar chord types:



	
Major (+ 2 voicings)
	
Minor (+ 2 voicings)
	
Major 7 (+ 2 voicings)
	
Minor 7 (+ 2 voicings)
	
Power Chord  - root and fifth note (+ 2 voicings)
	
Dominant 7 (+ 2 voicings)
	
Minor 7 flat 5 (+ 2 voicings)



This gives us 273 different guitar chord classes.
DOI: 10.5281/zenodo.7544247
title: IDMT-PIANO-MM Dataset

description: The dataset IDMT-PIANO-MM includes a total of 432 piano recordings (around four hours), which cover nine music pieces recorded in eight different rooms using six different recording devices. The pieces cover classical music (B. Bartók, W. A. Mozart, J. Pachelbel, and L. v. Beethoven) as well as jazz (S. Joplin as well as own compositions) and range from simple to medium difficulty. All music pieces are in the public domain. The recording locations range from small rooms to a large lecture hall. Information about the room geometries, piano position within the room, as well as wall materials are documented. The rooms include four different grand pianos, three upright pianos, and one stage-piano. At each location, audio recordings were made with three mobile phones (iPhone 6S Plus, Redmi Note 8, LG G6), two tablets (iPad Air 2, Amazon Fire tablet), and one stereo setup using two high-quality Oktava MK 012 microphones in an AB recording setup.
DOI: 10.5281/zenodo.7544248
title: IDMT-PIANO-MM Dataset

description: The dataset IDMT-PIANO-MM includes a total of 432 piano recordings (around four hours), which cover nine music pieces recorded in eight different rooms using six different recording devices. The pieces cover classical music (B. Bartók, W. A. Mozart, J. Pachelbel, and L. v. Beethoven) as well as jazz (S. Joplin as well as own compositions) and range from simple to medium difficulty. All music pieces are in the public domain. The recording locations range from small rooms to a large lecture hall. Information about the room geometries, piano position within the room, as well as wall materials are documented. The rooms include four different grand pianos, three upright pianos, and one stage-piano. At each location, audio recordings were made with three mobile phones (iPhone 6S Plus, Redmi Note 8, LG G6), two tablets (iPad Air 2, Amazon Fire tablet), and one stereo setup using two high-quality Oktava MK 012 microphones in an AB recording setup.
DOI: 10.5281/zenodo.7544109
title: IDMT-SMT-Guitar Dataset

description: The IDMT-SMT-GUITAR database is a large database for automatic guitar transcription. Seven different guitars in standard tuning were used with varying pick-up settings and different string measures to ensure a sufficient diversification in the field of electric and acoustic guitars. The recording setup consisted of appropriate audio interfaces, which were directly connected to the guitar output or in one case to a condenser microphone. The recordings are provided in one channel RIFF WAVE format with 44100 Hz sample rate.

The dataset consists of four subsets. The first contains all introduced playing techniques (plucking styles: finger-style, muted, picked; expression styles: normal, bending, slide, vibrato, harmonics, dead-notes) and is provided with a bit depth of 24 Bit. It has been recorded using three different guitars and consists of about 4700 note events with monophonic and polyphonic structure. As a particularity the recorded files contain realistic guitar licks ranging from monophonic to polyphonic instrument tracks.


The second subset of data consists of 400 monophonic and polyphonic note events each played with two different guitars. No expression styles were applied here and each note event was recorded and stored in a separate file with a bit depth of 16 Bit. The parameter annotations for the first and second subset are stored in XML format.


The third subset is made up of five short monophonic and polyphonic guitar recordings. All five pieces have been recorded with the same instrument and no special expression styles were applied. The files are stored with a bit depth of 16 Bit and each file is accompanied by a parameter annotation in XML format.


Additionally, a fourth subset is included, which was created for evaluation purposes in the context of chord recognition and rhythm style estimation tasks. This set contains recordings of 64 short musical pieces grouped by genre. Each piece has been recorded at two different tempi with three different guitars and is provided with a bit depth of 16 Bit. Annotations regarding onset positions, chords, rhythmic pattern length, and texture (monophony/polyphony) are included in various file formats.