id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_6fe3wiohwvaknjow5afof3i6gi	Sharon Webb	Mining Oral History Collections Using Music Information Retrieval Methods	2017	25	.pdf	application/pdf	6962	458	43	application of audio analysis techniques and methods to spoken word collections. – to treat digital audio files as text.1 We applied Music Information Retrieval (hereafter MIR) techniques to oral history interviews in order to develop new, complementary, approaches to text potential utility of MIR techniques to problems in both oral history and the digital humanities, 2 'Data-Mining the Audio of Oral History: A Workshop in Music Information Retrieval' at London College of Communication (March 2017) https://web.archive.org/web/20171003144121/http://www.techne.ac.uk/forstudents/techne-events/apr-2015/data-mining-the-audio-of-oral-history-a-workshop-in-music-information-retrieval https://web.archive.org/web/20171003144121/http:/www.techne.ac.uk/for-students/techne-events/apr-2015/data-mining-the-audio-of-oral-history-a-workshop-in-music-information-retrieval https://web.archive.org/web/20171003144121/http:/www.techne.ac.uk/for-students/techne-events/apr-2015/data-mining-the-audio-of-oral-history-a-workshop-in-music-information-retrieval collections using audio feature analysis and from delivering workshops on MIR in a digital decided to explore the potential for direct audio analysis of oral history interviews. The first workshop, 'Music Information Retrieval Algorithms for Oral History worked with digital audio files from the 'Archive of Resistance': a growing collection of oral collections).14 For example, digital audio files can be automatically described using high level Content based MIR combines methods of digital signal processing, machine learning and 	./cache/work_6fe3wiohwvaknjow5afof3i6gi.pdf	./txt/work_6fe3wiohwvaknjow5afof3i6gi.txt