# A survey of electric guitar related datasets

* [GUITAR-FX-DIST](https://zenodo.org/record/4296040)
* Release: November 30, 2020
* Reference:
* [Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks](https://arxiv.org/abs/2012.03216)
* Attribute:
* Samples length: 2 sec
* Unprocessed：
* 624 monophonic notes
* 420 polyphonic (2, 3 and 4 notes intervals and chords)
* 2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick)
* Schecter Diamond C-1 Classic
* Chester Stratocaster
* from [IDMT-SMT-Audio-Effects](https://www.idmt.fraunhofer.de/en/publications/datasets/audio_effects.html)
* Processed：
* Mono Discrete: ~160k
* Poly Discrete: ~110k
* Mono Continuous: 140k
* Poly Continuous: 140k
* the most common and representative settings a person might use

* [GuitarSet](https://zenodo.org/record/3371780)
* Release: 2018
* Reference:
* [Guitarset: A Dataset for Guitar Transcription](http://tomxi.weebly.com/uploads/1/2/1/6/121620128/xi_ismir_2018.pdf)
* Attribute:
* provide recordings of the individual strings
* time-aligned annotations of pitch contours, string and fret positions, chords, beats, downbeats, and playing style
* 360 excerpts that are close to 30 seconds in length
* annotation
- 6 pitch_contour annotations (1 per string)
- 6 midi_note annotations (1 per string)
- 1 beat_position annotation
- 1 tempo annotation
- 2 chord annotations (instructed and performed)*

* [AudioSet](https://research.google.com/audioset///dataset/electric_guitar.html)
* Release: 2017
* Reference:
* [Audio Set: An ontology and human-labeled dataset for audio events](https://research.google/pubs/pub45857/)
* Attribute:
* 10-seconds youtube clips
* estimated accuracy: 80%
* **Might be helpful to the solo detection**
* [GuitarSoloDetection](https://github.com/ashispati/GuitarSoloDetection)
* release: 2017
* reference:
* [AES International Conference on Semantic Audio. Audio Engineering Society, 2017](https://musicinformatics.gatech.edu/wp-content_nondefault/uploads/2017/06/Pati_Lerch_2017_A-Dataset-and-Method-for-Electric-Guitar-Solo-Detection-in-Rock-Music.pdf)
* Attribute:
* containing 60 full-length rock songs
* annotated the location of the guitar solos within the song
* **Might be useful for the solo detection**
* [IDMT-SMT-Guitar](https://www.idmt.fraunhofer.de/en/publications/datasets/guitar.html)
* Release: 2014
* Reference:
* [Automatic Tablature Transcription of Electric Guitar Recordings by Estimation of Score- and Instrument-related Parameters](https://www.semanticscholar.org/paper/Automatic-Tablature-Transcription-of-Electric-by-of-Kehling-Abe%C3%9Fer/b697dde224ef2894a129398b7e528a6feae2cf0c)
* Attribute:
* 7 different guitars in standard tuning and varying pick-up
* different string measures to ensure diversification in electric and acoustic guitars
* record with audio interfaces directly connected to the guitar output or in one case to a condenser microphone
* 4 subsets
* playing techniques
* 400 monophonic and polyphonic note events
* five short monophonic and polyphonic guitar recordings
* 64 short musical pieces grouped by genre. Each piece has been recorded at two different tempi
* **The third and the fourth subsets might be useful for the solo detection**
* [Guitar playing techniques dataset (GPT)](http://mac.citi.sinica.edu.tw/GuitarTranscription)
* release: 2014
* [Physically_augmented_guitar_chord_dataset](https://github.com/gerelmaab/Physically_augmented_guitar_chord_dataset)
* Release:
* Attribute:
* recorded directly from the guitar playing robot
* consists of 12 root notes and 10 types of chord quality in a total of 97 classes of chords
* Each chord was played individually with five types of stroking patterns.
* **This dataset is related to only chords that might not be suitable for the solo detection**