{"url":"/dataset/egfxset","name":"EGFxSet","full_name":"Electric Guitar Effects Dataset, ISMIR 2022 LBD","description_markdown":"EGFxSet (Electric Guitar Effects dataset) features recordings for all clean tones in a 22-fret Stratocaster, recorded with 5 different pickup configurations, also processed through 12 popular guitar effects.  Our dataset was recorded in real hardware, making it relevant for music information retrieval tasks on real music.  We also include annotations for parameter settings of the effects we used.\r\n\r\nEGFxSet is a dataset of 8,970 audio files with a 5-second duration each, summing a total time of  12 hours and 28 minutes.\r\n\r\nAll possible 138 notes of a standard tuning 22 frets guitar were recorded in each one of the 5 pickup configurations, giving a total of 690 clean tone audio files ( 58 min ).\r\n\r\nThe 690 clean audio (58 min) files were processed through 12 different audio effects employing actual guitar gear (no VST emulations were used), summing a total of 8,280 processed audio files (11 hours 30 min).\r\n\r\nThe effects employed were divided into four categories, and each category comprised three different effects. Some gear used supported the generation of more than one effect setting (but only one was recorded at a time).\r\n\r\nCategories, Models and Effects: Distortion (Blues Driver, Tube Screamer, RAT2  Distortion), Modulation (Chorus, Phaser, Flanger), Delays (Digital Delay, Tape Echo, Sweep Echo), Reverb (Plate Reverb, Hall Reverb, Spring Reverb).\r\n\r\nAnnotations are labeled by a trained electric guitar musician. For each tone, we provide: Guitar string number, Fret number, Guitar pickup configuration, Effect name, Effect type, Hardware modes, Knob names, Knob types, Knob settings).\r\n\r\nThe dataset website is: https://egfxset.github.io/\r\n\r\nThe data can be accessed here: https://zenodo.org/record/7044411#.YxKdSWzMKEI\r\n\r\nAn ISMIR extended abstract was presented in 2022: https://ismir2022.ismir.net/program/lbd/\r\n\r\nThis dataset was conceived during Iran Roman's \"Deep Learning for Music Information Retrieval\" course imparted in the postgraduate studies in music technology at the UNAM (Universidad Nacional Autónoma de México). The result is a combined effort between two UNAM postgraduate students (Hegel Pedroza and Gerardo Meza) and Iran Roman (NYU).","description_withheld":null,"homepage":"https://egfxset.github.io","introduced_date":"2022-12-09","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["EGFxSet"],"data_loaders":[{"repo":"https://github.com/mir-dataset-loaders/mirdata","url":"https://mirdata.readthedocs.io/en/latest/source/mirdata.html#module-mirdata.datasets.egfxset","frameworks":[]}],"num_papers_in_archive":13,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}