A survey of electric guitar related datasets

• GUITAR-FX-DIST

  • Release: November 30, 2020
  • Reference:
    • Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks
  • 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
    • Processed：
      • Mono Discrete: ~160k
      • Poly Discrete: ~110k
      • Mono Continuous: 140k
      • Poly Continuous: 140k
      • the most common and representative settings a person might use

• GuitarSet

  • Release: 2018
  • Reference:
    • Guitarset: A Dataset for Guitar Transcription
  • 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

  • Release: 2017
  • Reference:
    • Audio Set: An ontology and human-labeled dataset for audio events
  • Attribute:
    • 10-seconds youtube clips
    • estimated accuracy: 80%
  • Might be helpful to the solo detection

• GuitarSoloDetection

  • release: 2017
  • reference:
    • AES International Conference on Semantic Audio. Audio Engineering Society, 2017
  • 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

  • Release: 2014
  • Reference:
    • Automatic Tablature Transcription of Electric Guitar Recordings by Estimation of Score- and Instrument-related Parameters
  • 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)

  • release: 2014

• 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