Print Email Facebook Twitter Revealing Hidden Conversations in Privacy-Sensitive Audio Using Neural Networks Title Revealing Hidden Conversations in Privacy-Sensitive Audio Using Neural Networks Author Vunderink, Pepijn (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Hung, H.S. (mentor) Vargas Quiros, J.D. (mentor) Baaijens, J.A. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2022-06-24 Abstract With widespread use of advanced technology for the recording, storing and sharing of social interactions, protecting privacy of people has been a growing concern. This paper zooms in on the collection of spoken audio with regard for the privacy of recorded individuals. Recently efforts have been made to collect audio at a low sampling rate to obfuscate spoken words in the recorded audio, such that conversations are kept private. This research investigates whether it is possible to upsample this low-resolution audio, using an existing super-resolution model, in order to reveal parts of the previously obfuscated conversations. The performance of the model is measured in terms of the word error rate of automatically generated transcriptions of the upsampled audio. It turns out that it is possible to significantly increase the intelligibility of low resolution privacy-sensitive audio by upsampling. Though the use of the super-resolution model seems to be limited when it comes to revealing significant parts of conversations. Subject super resolutionprivacyaudiosuper-resolutionbandwidth extensionsartificial bandwidth extension To reference this document use: http://resolver.tudelft.nl/uuid:ebd4d2bb-ddfb-470b-93f3-72a18e2b6fe8 Part of collection Student theses Document type bachelor thesis Rights © 2022 Pepijn Vunderink Files PDF RP_Thesis_Pepijn_Vunderink.pdf 1.48 MB Close viewer /islandora/object/uuid:ebd4d2bb-ddfb-470b-93f3-72a18e2b6fe8/datastream/OBJ/view