"""EigenScape Dataset Loader
.. admonition:: Dataset Info
:class: dropdown
**EigenScape: a database of spatial acoustic scene recordings**
*Created By:*
| Marc Ciufo Green, Damian Murphy.
| Audio Lab, Department of Electronic Engineering, University of York.
Version raw
*Description:*
EigenScape is a database of acoustic scenes recorded spatially using the mh Acoustics EigenMike.
All scenes in this format are in Raw format (A-format) with 32 channels
The database contains recordings of eight different location classes: Beach, Busy Street, Park, Pedestrian Zone, Quiet Street, Shopping Centre, Train Station, Woodland.
The recordings were made in May 2017 at sites across the North of England.
*Audio Files Included:*
* 8 different examples of each location class were recorded over a duration of 10 minutes
* 64 recordings in total.
* EigenMike channel ordering (32 total) with calibration and PGA level (captured with firewire interface and EigenStudio). 24-bit / 48 kHz resolution.
*Annotations Included:*
* No event labels associated with this dataset
* The metadata file gives more tempogeographic detail on each recording
* the EigenScape `recording map <http://bit.ly/EigenSMap>`_ shows the locations and classes of all the recordings.
* No predefined training, validation, or testing splits.
*Please Acknowledge EigenScape in Academic Research:*
If you use this dataset please cite its original publication:
.. code-block:: latex
Green MC, Murphy D. EigenScape: A database of spatial acoustic scene recordings. Applied Sciences. 2017 Nov;7(11):1204.
*License:*
* Creative Commons Attribution 4.0 International
*Important:*
* Use with caution. This loader "Engineers" a solution to obtain the correct files after Park6 and Park8 got mixed-up at the `eigenscape` and `eigenscape_raw` remotes. See the REMOTES and index if you want to understand how this engineered solution works. Also see the discussion about this engineered solution with the dataset author https://github.com/micarraylib/micarraylib/issues/8#issuecomment-1105357329
"""
import os
from typing import BinaryIO, Optional, TextIO, Tuple
import librosa
import numpy as np
import csv
import jams
import glob
import numbers
from itertools import cycle
from soundata import download_utils, jams_utils, core, annotations, io
BIBTEX = """
@article{green2017eigenscape,
title={EigenScape: A database of spatial acoustic scene recordings},
author={Green, Marc Ciufo and Murphy, Damian},
journal={Applied Sciences},
volume={7},
number={11},
pages={1204},
year={2017},
publisher={Multidisciplinary Digital Publishing Institute}
}
"""
REMOTES = {
"Beach": download_utils.RemoteFileMetadata(
filename="Beach.zip",
url="https://webfiles.york.ac.uk/INFODATA/eaeaac50-483e-408f-a391-01b02d4ff9c4/Beach.zip",
checksum="3f03a527291b5aedffb436fe4f12774b",
),
"BusyStreet": download_utils.RemoteFileMetadata(
filename="BusyStreet.zip",
url="https://webfiles.york.ac.uk/INFODATA/eaeaac50-483e-408f-a391-01b02d4ff9c4/BusyStreet.zip",
checksum="7561ef835b4f43c5f0fa637a9747bc41",
),
"Park_york": download_utils.RemoteFileMetadata(
filename="Park.zip",
url="https://webfiles.york.ac.uk/INFODATA/eaeaac50-483e-408f-a391-01b02d4ff9c4/Park.zip",
checksum="081417d792a31c7cdada5442227c796c",
),
"Park_zenodo": download_utils.RemoteFileMetadata(
filename="Park.zip",
url="https://zenodo.org/record/1284156/files/Park.zip?download=1",
checksum="1268c7d8057529672d3cc17bec8ae302",
),
"PedestrianZone": download_utils.RemoteFileMetadata(
filename="PedestrianZone.zip",
url="https://webfiles.york.ac.uk/INFODATA/eaeaac50-483e-408f-a391-01b02d4ff9c4/PedestrianZone.zip",
checksum="9b6cf5afefa7779eb69d6764d680801c",
),
"Metadata-EigenScape": download_utils.RemoteFileMetadata(
filename="Metadata-EigenScape.csv",
url="https://zenodo.org/record/1284156/files/Metadata-EigenScape.csv?download=1",
checksum="cbed105fb56604c4b763788690089d55",
),
}
wav_md5_dict = {
"QuietStreet-01-Raw": "4b92a30ca73edb12f9190fde53cadcba",
"QuietStreet-02-Raw": "15b7742bacadb0417b227de9550af725",
"QuietStreet-03-Raw": "b4f6dc1cb65f9b2051273e496764219d",
"QuietStreet-04-Raw": "7c45aaca071c2b9cee3849f73be97998",
"QuietStreet-05-Raw": "74fc68ce1d552b3c76162809bf343750",
"QuietStreet-06-Raw": "bb6f09247cf62fecc29349aca10500bd",
"QuietStreet-07-Raw": "a1026a16ba0f4ebcbae0bff6f78c3c5c",
"QuietStreet-08-Raw": "69c5376a43357262c950393a5766698d",
"ShoppingCentre-01-Raw": "c02ca658b0cf85561f8c5f728db5bee9",
"ShoppingCentre-02-Raw": "951c77f0867a09d18d5d14c52932b5d2",
"ShoppingCentre-03-Raw": "a3ef52bc54d0c7738be537f3b28be7c6",
"ShoppingCentre-04-Raw": "bb040c0dce13eb553458f014644061f4",
"ShoppingCentre-05-Raw": "eaf0b9c5313d5bceb0355ab989d04a03",
"ShoppingCentre-06-Raw": "7e402f7412a95d295d40fa05cb6096b6",
"ShoppingCentre-07-Raw": "0c10d361a1e2b1a150f48d869580cc00",
"ShoppingCentre-08-Raw": "38f58d35833aab2a2bbf62c1388c2fb3",
"TrainStation-01-Raw": "b7c9ab43daa0f7ba940c44e0b9591e67",
"TrainStation-02-Raw": "104723e307c61a501d24033fac0ea205",
"TrainStation-03-Raw": "651a9c536d5ee12f955998ba9fc0d63c",
"TrainStation-04-Raw": "6326b1ab69a2ff306978741937ce2b7a",
"TrainStation-05-Raw": "fe137aefe8d769b084bd0ff1949d0400",
"TrainStation-06-Raw": "60932c5d02c5907550a47924e349f47e",
"TrainStation-07-Raw": "03a56d202ab8a3851799fe616add38b6",
"TrainStation-08-Raw": "de418fdd958ed9875630e1fa65417b1e",
"Woodland-01-Raw": "b451fd7e167268d7308aeee95e632829",
"Woodland-02-Raw": "cd7f344fc6085c8385f1323c92049a79",
"Woodland-03-Raw": "78c7661bf21225308183c67e903a229f",
"Woodland-04-Raw": "91afe627730952d75401277d851a67a3",
"Woodland-05-Raw": "f22e3a06cef4e9dd81b44b2f418b0ef7",
"Woodland-06-Raw": "84dec0b311d153a3757ca7582f9d12fb",
"Woodland-07-Raw": "eef91d59e41d41a7e0e7c035893dcb55",
"Woodland-08-Raw": "0541c709206b1cb4971969cb6d8dab0a",
}
nrecs = 8
wav_files = ["QuietStreet", "ShoppingCentre", "TrainStation", "Woodland"]
for f in wav_files:
for i in range(nrecs):
remote_id = f + "-0" + str(i + 1) + "-Raw"
REMOTES[remote_id] = download_utils.RemoteFileMetadata(
filename=f + "-0" + str(i + 1) + "-Raw.wav",
url="https://webfiles.york.ac.uk/INFODATA/eaeaac50-483e-408f-a391-01b02d4ff9c4/"
+ f
+ "-0"
+ str(i + 1)
+ "-Raw.wav",
checksum=wav_md5_dict[remote_id],
)
LICENSE_INFO = """
Creative Commons Attribution 4.0 International
"""
[docs]class Clip(core.Clip):
"""Eigenscape Raw Clip class
Args:
clip_id (str): id of the clip
Attributes:
audio_path (str): path to the audio file
additional information (str): notes included by the dataset
authors with other details relevant to the specific clip
clip_id (str): clip id
date (str): date when the audio signal was recorded
location (str): city were the audio signal was recorded
tags (soundata.annotation.Tags): tag (scene label) of the clip + confidence.
time (str): time when the audio signal was recorded
"""
def __init__(self, clip_id, data_home, dataset_name, index, metadata):
super().__init__(clip_id, data_home, dataset_name, index, metadata)
self.audio_path = self.get_path("audio")
@property
def audio(self) -> Optional[Tuple[np.ndarray, float]]:
"""The clip's audio
Returns:
* np.ndarray - audio signal
* float - sample rate
"""
return load_audio(self.audio_path)
@property
def tags(self):
"""The clip's tags
Returns:
* annotations.Tags - Tags (scene label) of the clip + confidence.
"""
scene_label = self._clip_metadata.get("scene_label")
if scene_label is None:
return None
else:
return annotations.Tags([scene_label], "open", np.array([1.0]))
@property
def location(self):
"""The clip's location.
Returns:
* str - Tags annotation object
"""
return self._clip_metadata.get("location")
@property
def time(self):
"""The clip's time (00:00-23:59).
Returns:
* str - time when the audio signal was recorded
"""
return self._clip_metadata.get("time")
@property
def date(self):
"""The clip's date.
Returns:
* str - date when the audio signal was recorded
"""
return self._clip_metadata.get("date")
@property
def additional_information(self):
"""The clip's additional information.
Returns:
* str - notes included by the dataset authors with other details relevant to the specific clip
"""
return self._clip_metadata.get("additional information")
[docs] def to_jams(self):
"""Get the clip's data in jams format
Returns:
jams.JAMS: the clip's data in jams format
"""
return jams_utils.jams_converter(
audio_path=self.audio_path, tags=self.tags, metadata=self._clip_metadata
)
[docs]@io.coerce_to_bytes_io
def load_audio(fhandle: BinaryIO, sr=None) -> Tuple[np.ndarray, float]:
"""Load an EigenScape Raw audio file
Args:
fhandle (str or file-like): file-like object or path to audio file
sr (int or None): sample rate for loaded audio, None by default, which
uses the file's original sampling rate of 48000 without resampling.
Returns:
* np.ndarray - the audio signal
* float - The sample rate of the audio file
"""
audio, sr = librosa.load(fhandle, sr=sr, mono=False)
return audio, sr
[docs]@core.docstring_inherit(core.Dataset)
class Dataset(core.Dataset):
"""The EigenScape Raw dataset"""
def __init__(self, data_home=None):
super().__init__(
data_home,
name="eigenscape_raw",
clip_class=Clip,
bibtex=BIBTEX,
remotes=REMOTES,
license_info=LICENSE_INFO,
)
[docs] @core.copy_docs(load_audio)
def load_audio(self, *args, **kwargs):
return load_audio(*args, **kwargs)
@core.cached_property
def _metadata(self):
metadata_path = os.path.join(self.data_home, "Metadata-EigenScape.csv")
if not os.path.exists(metadata_path):
raise FileNotFoundError("Metadata not found. Did you run .download()?")
metadata_index = {}
with open(metadata_path) as csv_file:
csv_reader = csv.reader(csv_file, delimiter=",")
next(csv_reader)
for row in csv_reader:
file_name = os.path.basename(row[0])
clip_id = (os.path.basename(file_name).replace(".wav", "")) + "-Raw"
scene_label = row[1]
location = row[2]
time = row[3]
date = row[4]
additional_information = row[5]
metadata_index[clip_id] = {
"scene_label": scene_label,
"location": location,
"time": time,
"date": date,
"additional information": additional_information,
}
return metadata_index