This LaraHarris_MSOS_Readme.txt file was generated on 2024-06-13 by Lara Harris.


GENERAL INFORMATION

1. Title of Dataset: Making Sense Of Sounds: Data for the machine learning challenge 2018

More info about the dataset and challenge is here: https://cvssp.org/projects/making_sense_of_sounds/site/challenge/

2. Author Information
	A. Principal Investigator Contact Information
		Name: Prof. Mark Plumbley
		Institution: Centre for Vision, Speech and Signal Processing (CVSSP)
		Address: University of Surrey, Guildford, Surrey, GU2 7XH, UK
		Email: 

	B. Associate or Co-investigator Contact Information
		Name: Prof. Bill Davies
		Institution: Acoustics Research Centre
		Address: University of Salford, Salford M5 4WT, UK
		Email: 

	C. Alternate Contact Information
		Name: Dr Lara Harris
		Institution: Institute for Advanced Manufacturing and Engineering
		Address: Coventry University, UK
		Email: ae0192@coventry.ac.uk

3. Date of data collection (single date, range, approximate date) <suggested format YYYY-MM-DD>: 
2018

4. Geographic location of data collection <latitude, longiute, or city/region, State, Country, as appropriate>: 
Salford, Greater Manchester, UK

5. Information about funding sources that supported the collection of the data: 
Making Sense of Sounds EP/N014111/1



SHARING/ACCESS INFORMATION

1. Licenses/restrictions placed on the data: 
It should be assumed that all files in this challenge are provided under the licence
CC-BY-NC 4.0 (Creative Commons, Attribution Noncommercial). This is the most restrictive licence of any file in the dataset, though some were also provided under CC0 and CC-BY. A complete listing of the exact licences and author attributions is in the final file listing.

2. Links to publications that cite or use the data: 
https://doi.org/10.1109/ICASSP.2019.8683292
https://doi.org/10.1038/s42003-023-05040-5


3. Links to other publicly accessible locations of the data: 
https://salford.figshare.com/articles/dataset/Making_Sense_Of_Sounds_Data_for_the_machine_learning_challenge_2018/6901475/4


4. Links/relationships to ancillary data sets: 

5. Was data derived from another source? yes/no
	A. If yes, list source(s): 
Yes: The audio files were taken from Freesound data base, the ESC-50 dataset and the Cambridge-MT Multitrack Download Library.


6. Recommended citation for this dataset: 
Harris, Lara; Bones, Oliver Charles (2018). Making Sense Of Sounds: Data for the machine learning challenge 2018. University of Salford. Dataset. https://doi.org/10.17866/rd.salford.6901475.v4

DATA & FILE OVERVIEW

1. File List: 
The dataset download (zip file) contains a full listing of all files in csv format. 

2. Relationship between files, if important: 

3. Additional related data collected that was not included in the current data package: 
See details on the figshare page:
https://salford.figshare.com/articles/dataset/Making_Sense_Of_Sounds_Data_for_the_machine_learning_challenge_2018/6901475/4



4. Are there multiple versions of the dataset? yes/no
	A. If yes, name of file(s) that was updated: 
		i. Why was the file updated? 
		ii. When was the file updated? 
Yes - Latest is version 4. Files were released over several months as the dataset was for a machine learning challenge. In early stages we witheld certain files that could influence outcome of the challenge. The final set contains full file listing names and categories (classifications that the challenge entrants were tyring to predict).

METHODOLOGICAL INFORMATION

1. Description of methods used for collection/generation of data: 
The paper has some brief details on ocllection and processing:
https://doi.org/10.1109/ICASSP.2019.8683292

Files were sourced from elsewhere then processed to form the final datatset.


2. Methods for processing the data: 
<describe how the submitted data were generated from the raw or collected data>

3. Instrument- or software-specific information needed to interpret the data: 
<include full name and version of software, and any necessary packages or libraries needed to run scripts>

4. Standards and calibration information, if appropriate: 

5. Environmental/experimental conditions: 

6. Describe any quality-assurance procedures performed on the data: 
Humans listenened to the files to check the category label was correct. 
Autoated tests were written and run in MATLAB to check non-subjective aspects e.g. that each file had a unique name and appeared only once in the dataset. 

7. People involved with sample collection, processing, analysis and/or submission: 
Lara Harris
Olly Bones
Zuzanna Podwinska
Will Bailey
Alex Wilson
Trevor Cox
Bill Davies
[all Salford University]

DATA-SPECIFIC INFORMATION FOR: [FILENAME]
<repeat this section for each dataset, folder or file, as appropriate>

1. Number of variables: 

2. Number of cases/rows: 

3. Variable List: 
<list variable name(s), description(s), unit(s)and value labels as appropriate for each>

4. Missing data codes: 
<list code/symbol and definition>n

5. Specialized formats or other abbreviations used: