You can use the car sound dataset with this Python package.
Project description
car_sound_dataset
This repo contains the source code of car sound dataset Python package.
https://pypi.org/project/car-sound-dataset/
How to install?
pip install car-sound-dataset
About the dataset
The dataset contains 17121 car sound events. Each event is recorded by at least 1 and maximum 6 devices. The length of each recording is 3 sec. The sample frequency was 3906 Hz during the measurements. You can download the dataset and select different recordings with this package.
Class
A class used to represent the car sound dataset.
...
Attributes
----------
path : str
A path, where you want to download and use the dataset.
The default path is the current working directory.
Methods
download_and_get_dataset()
Downloads the dataset and the json file, which identifies the events.
list_events_of_dict( car_dataset_dict)
This method prints to console the event IDs of a car_dataset_dict.
plot_event(car_dataset_dict, event_id)
You can plot an event with this method.
play_event(car_dataset_dict, event_id, nomralize=True)
You can play the recordings of an event with this method.
filter_by_pattern(car_dataset_dict, pattern)
This method helps you to select events by a pattern.
The pattern should contain 'X', '0' or '1'.
'X' - You don't care, if the device recorded the event or not.
'0' - You skip the recordings of this device.
'1' - This device recorded the event.
You have to give the pattern as a list. Each list element represents each device.
An example pattern: ['0', '1', 'X', '0', 'X', '1']
This means we skip the recordings of DEVICE1 and DEVICE4. We select the events,
where DEVICE2 and DEVICE6 recorded the event. Each event will contain 2-4 recordings.
It depends on DEVICE3 and DEVICE5. If one of DEVICE3 and DEVICE5 recorded the event,
the event will contain 3 recordings and if both is recorded the event, it will contain
4 recordings.
filter_by_exact_device_count(car_dataset_dict, device_count)
This method selects the events, which is recorded by the given count of devices.
filter_by_min_device_count(car_dataset_dict, device_count)
This method selects the events, which is recorded by at least the given count of devices.
load_dict_as_pool(car_dataset_dict)
This method loads the recordings into a 3D numpy array. In this case each element
contains only one recording. The recordings of an event are split to single recordings.
load_dict(car_dataset_dict, is_random = False, device_indexes = [])
This method loads the recordings into a 3D numpy array. In this case each element
contains events. One event usually contains more than one recordings.
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