Print Email Facebook Twitter Laughter detection in privacy-sensitive audio Title Laughter detection in privacy-sensitive audio Author Fregonara, Matteo (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 the development of new technologies and approaches in the field of social signal processing, concerns regarding privacy around recording conversations have arised. One of the main ways to preserve the privacy of the speakers in recorded conversations consists of decimating said conversations, which consists of reducing the sample frequency and the frequency bandwidth of the audio. This theoretically makes the verbal content of the conversation (the words themselves) unintelligible, while still preserving other useful non-verbal social cues such as laughter, pitch modulation and frequency of speech, amongst others. However, this has not been experimentally verified. This research paper addresses this knowledge gap by exploring the performance of laughter detection machine learning models with decimated audio. An existing pre-trained state-of-the-art laughter detection model was employed and its performance was evaluated for a dataset of decimated audio with sample frequencies ranging from 300Hz to 44100Hz. To reference this document use: http://resolver.tudelft.nl/uuid:ac1aca59-1812-4d3f-99a5-9441f61a6d9a Part of collection Student theses Document type bachelor thesis Rights © 2022 Matteo Fregonara Files PDF research_paper_5_.pdf 751.23 KB Close viewer /islandora/object/uuid:ac1aca59-1812-4d3f-99a5-9441f61a6d9a/datastream/OBJ/view