"""EGFxSet Dataset Loader
.. admonition:: Dataset Info
:class: dropdown
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.
EGFxSet is a dataset of 8,970 audio files with a 5-second duration each,
summing a total time of - 12 hours and 28 minutes -.
All 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 ).
The 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).
The effects employed were divided into four categories, and each category comprised three different effects.
Sometimes there were employed more than one effect from a same guitar equipment.
Categories, Models and Effects:
Distortion:
Boss BD-2:
Blues Driver
Ibanez Minitube Screamer:
Tube Screamer
ProCo RAT2:
Distortion
Modulation:
Boss CE-3:
Chorus
MXR Phase 45:
Phaser
Mooer E-Lady:
Flanger
Delays:
Line6 DL-4:
Digital Delay,
Tape Echo,
Sweep Echo
Reverb:
Orange CR-60 Combo Amplifier:
Plate Reverb,
Hall Reverb,
Spring Reverb
Annotations 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
The dataset website is: https://egfxset.github.io/
The data can be accessed here: https://zenodo.org/record/7044411#.YxKdSWzMKEI
An ISMIR extended abstract was presented in 2022: https://ismir2022.ismir.net/program/lbd/
This 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).
"""
import csv
import os
from typing import BinaryIO, Optional, Tuple
from ast import literal_eval
import re
import librosa
import numpy as np
from smart_open import open
from mirdata import annotations, core, download_utils, jams_utils, io
BIBTEX = """
@techreport{pedroza2022egfxset,
title={EGFxSet: Electric guitar tones processed through real effects of distortion, modulation, delay and reverb},
author={Pedroza, Hegel and Meza, Gerardo and Roman, Iran},
year={2022},
institution={UNAM},
booktitle={Extended Abstracts for the Late-Breaking Demo Session of the 23rd Int. Society for Music Information Retrieval Conf., Bengaluru, India, 2022.},
}
"""
INDEXES = {
"default": "1",
"test": "1",
"1": core.Index(filename="egfxset_index_1.json"),
}
REMOTES = {
"bluesDriver": download_utils.RemoteFileMetadata(
filename="BluesDriver.zip",
url="https://zenodo.org/record/7044411/files/BluesDriver.zip?download=1",
checksum="b1d6dce9064a25a1cff2a0c40c30a2e4",
),
"chorus": download_utils.RemoteFileMetadata(
filename="Chorus.zip",
url="https://zenodo.org/record/7044411/files/Chorus.zip?download=1",
checksum="3698b3b1756917f93dadf27517d48479",
),
"clean": download_utils.RemoteFileMetadata(
filename="Clean.zip",
url="https://zenodo.org/record/7044411/files/Clean.zip?download=1",
checksum="cdb1b401960f56becc8640387910e78a",
),
"digitalDelay": download_utils.RemoteFileMetadata(
filename="Digital-Delay.zip",
url="https://zenodo.org/record/7044411/files/Digital-Delay.zip?download=1",
checksum="4a25d57bcb0083667bade7b3c42460bc",
),
"flanger": download_utils.RemoteFileMetadata(
filename="Flanger.zip",
url="https://zenodo.org/record/7044411/files/Flanger.zip?download=1",
checksum="f3f7b39c895a400d35c5b1314a1122bd",
),
"hallReverb": download_utils.RemoteFileMetadata(
filename="Hall-Reverb.zip",
url="https://zenodo.org/record/7044411/files/Hall-Reverb.zip?download=1",
checksum="c173bebdcbed50d4bc8803e0b30d6517",
),
"phaser": download_utils.RemoteFileMetadata(
filename="Phaser.zip",
url="https://zenodo.org/record/7044411/files/Phaser.zip?download=1",
checksum="1842e2643dd34d7285a77506ca540df3",
),
"plateReverb": download_utils.RemoteFileMetadata(
filename="Plate-Reverb.zip",
url="https://zenodo.org/record/7044411/files/Plate-Reverb.zip?download=1",
checksum="abbcc68d692f323e8af6aeb8d478c40d",
),
"rat": download_utils.RemoteFileMetadata(
filename="RAT.zip",
url="https://zenodo.org/record/7044411/files/RAT.zip?download=1",
checksum="afe9fc757a51d04126c23159706f4e8e",
),
"spring-Reverb": download_utils.RemoteFileMetadata(
filename="Spring-Reverb.zip",
url="https://zenodo.org/record/7044411/files/Spring-Reverb.zip?download=1",
checksum="21afc47594ed8f37db008f313e50c634",
),
"sweepEcho": download_utils.RemoteFileMetadata(
filename="Sweep-Echo.zip",
url="https://zenodo.org/record/7044411/files/Sweep-Echo.zip?download=1",
checksum="ea6dda440e9af6a19173facdf2bf17ac",
),
"tapeEcho": download_utils.RemoteFileMetadata(
filename="TapeEcho.zip",
url="https://zenodo.org/record/7044411/files/TapeEcho.zip?download=1",
checksum="77adf4a6a8ed4eb566b2b8e77735c7dc",
),
"tubeScreamer": download_utils.RemoteFileMetadata(
filename="TubeScreamer.zip",
url="https://zenodo.org/record/7044411/files/TubeScreamer.zip?download=1",
checksum="b9c46ed65037d0bd17bdf82dc3125beb",
),
"metadata": download_utils.RemoteFileMetadata(
filename="egfxset_metadata.csv",
url="https://zenodo.org/record/7044411/files/egfxset_metadata.csv?download=1",
checksum="ec8d160fe79469c7de8cad528d7d35e1",
),
}
LICENSE_INFO = "Creative Commons Attribution 4.0 International"
[docs]class Track(core.Track):
"""EGFxSet Track class
Args:
track_id (str): track id of the track
Attributes:
audio_path (str): path to the track's audio file
stringfret_tuple (list): an array with the tuple of the note recorded
pickup_configuration (string): the pickup used in the recording
effect (str): the effect recorded
model (str): the model of the hardware used
effect_type (str) the type of effect used (distortion, modulation, delay or reverb)
knob_names (list): an array with the knob names of the effect used or "None" when the recording is a clean effect sound
knob_type (list): an array with the type of knobs of the effect used or "None" when the recording is a clean effect sound
setting (list): the setting of the effect recorded or "None" when the recording is a clean effect sound
Cached Properties:
note_name (ndarray): a list with the note name annotation of the audio file (e.g. "Ab5", "C6", etc.)
midinote (NoteData): the midinote annotation of the audio file
"""
def __init__(
self,
track_id,
data_home,
dataset_name,
index,
metadata,
):
super().__init__(
track_id,
data_home,
dataset_name,
index,
metadata,
)
self.audio_path = self.get_path("audio")
@property
def stringfret_tuple(self):
return self._track_metadata.get("String-fret Tuple")
@core.cached_property
def note_name(self) -> Optional[annotations.NoteData]:
return self._track_metadata.get("Note Name")
@core.cached_property
def midinote(self) -> Optional[annotations.NoteData]:
return self._track_metadata.get("Midinote")
@property
def pickup_configuration(self):
return self._track_metadata.get("Pickup Configuration")
@property
def effect(self):
return self._track_metadata.get("Effect")
@property
def model(self):
return self._track_metadata.get("Model")
@property
def effect_type(self):
return self._track_metadata.get("Effect Type")
@property
def knob_names(self):
return self._track_metadata.get("Knob Names")
@property
def knob_type(self):
return self._track_metadata.get("Knob Type")
@property
def setting(self):
return self._track_metadata.get("Setting")
@property
def audio(self) -> Optional[Tuple[np.ndarray, float]]:
"""Solo guitar audio (mono)
Returns:
* np.ndarray - audio signal
* float - sample rate
"""
return load_audio(self.audio_path)
[docs] def to_jams(self):
"""Get the track's data in jams format
Returns:
jams.JAMS: the track's data in jams format
"""
return jams_utils.jams_converter(
audio_path=self.audio_path, metadata=self._track_metadata
)
[docs]@io.coerce_to_bytes_io
def load_audio(fhandle: BinaryIO) -> Tuple[np.ndarray, float]:
"""Load EGFxSet guitar audio
Args:
fhandle (str or file-like): File-like object or path to audio file
Returns:
* np.ndarray - audio signal
* float - sample rate
"""
return librosa.load(fhandle, sr=None, mono=True)
[docs]@core.docstring_inherit(core.Dataset)
class Dataset(core.Dataset):
"""
The EGFxSet dataset
"""
def __init__(self, data_home=None, version="default"):
super().__init__(
data_home,
version,
name="egfxset",
track_class=Track,
bibtex=BIBTEX,
indexes=INDEXES,
remotes=REMOTES,
license_info=LICENSE_INFO,
)
@core.cached_property
def _metadata(self):
metadata_path = os.path.join(self.data_home, "egfxset_metadata.csv")
metadata_index = {}
tracknames = self.track_ids
cuerdas = {"1": 64, "2": 59, "3": 55, "4": 50, "5": 45, "6": 40}
file_handle = open(metadata_path, "r")
reader = list(csv.DictReader(file_handle))
indexname = []
for name in reader:
indexname.append(name["Effect "].split(" ")[0])
for track in tracknames:
if track[:3] == "RAT":
trackiden = track[:3].lower()
if track[:3] != "RAT":
trackiden = re.findall("[A-Z][^A-Z]*", track)[0].lower()
trackiden = trackiden.replace(" ", "")
trackiden = trackiden.replace("_", "")
noteCord = track.split("/")[1].split("-")
if len(noteCord) != 2:
noteCord = track.split("/")[1].split(".")
if track[:2].lower() == "cl":
metadata_index[track] = {
"String-fret Tuple": [
int(s) for s in re.findall(r"\b\d+\b", track.split("/")[1])
],
"Note Name": annotations.convert_pitch_units(
pitches=np.array(
[(cuerdas[noteCord[0]] + int(float(noteCord[1])))],
dtype=float,
),
pitch_unit="midi",
target_pitch_unit="note_name",
),
"Midinote": annotations.NoteData(
intervals=np.array([[0, 5]], dtype=float),
interval_unit="s",
pitches=np.array(
[(cuerdas[noteCord[0]] + int(float(noteCord[1])))],
dtype=float,
),
pitch_unit="midi",
),
"Pickup Configuration": track.split("_")[1].split("/")[0],
"Effect": "clean",
"Model": "None",
"Effect Type": "None",
"Knob Names": "None",
"Knob Type": "None",
"Setting": "None",
}
if trackiden in indexname:
metadata_index[track] = {
"String-fret Tuple": [
int(s) for s in re.findall(r"\b\d+\b", track.split("/")[1])
],
"Note Name": annotations.convert_pitch_units(
pitches=np.array(
[(cuerdas[noteCord[0]] + int(float(noteCord[1])))],
dtype=float,
),
pitch_unit="midi",
target_pitch_unit="note_name",
),
"Midinote": annotations.NoteData(
intervals=np.array([[0, 5]], dtype=float),
interval_unit="s",
pitches=np.array(
[(cuerdas[noteCord[0]] + int(float(noteCord[1])))],
dtype=float,
),
pitch_unit="midi",
),
"Pickup Configuration": track.split("_")[1].split("/")[0],
"Effect": reader[indexname.index(trackiden)]["Effect "],
"Model": reader[indexname.index(trackiden)]["Model"],
"Effect Type": reader[indexname.index(trackiden)]["Effect Type"],
"Knob Names": literal_eval(
reader[indexname.index(trackiden)]["Knob Names"]
),
"Knob Type": literal_eval(
reader[indexname.index(trackiden)]["Knob Type"]
),
"Setting": literal_eval(
reader[indexname.index(trackiden)]["Setting "]
),
}
return metadata_index