{"data":{"id":"10.13016/m2d885","type":"dois","attributes":{"doi":"10.13016/m2d885","prefix":"10.13016","suffix":"m2d885","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Luo, Yuancheng","nameType":"Personal","givenName":"Yuancheng","familyName":"Luo","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Fast Numerical and Machine Learning Algorithms for Spatial Audio Reproduction"}],"publisher":"Digital Repository at the University of Maryland","container":{},"publicationYear":2014,"subjects":[{"subject":"Computer science","subjectScheme":"pqcontrolled"},{"subject":"Computer engineering","subjectScheme":"pqcontrolled"},{"subject":"Mathematics","subjectScheme":"pqcontrolled"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Active-Learning","subjectScheme":"pquncontrolled"},{"subject":"Gaussian Processes","subjectScheme":"pquncontrolled"},{"subject":"Head-related Transfer Functions","subjectScheme":"pquncontrolled"},{"subject":"Non-negative least squares","subjectScheme":"pquncontrolled"},{"subject":"Non-negative Matrix Factorization","subjectScheme":"pquncontrolled"},{"subject":"Sound-source Localization","subjectScheme":"pquncontrolled"}],"contributors":[],"dates":[{"date":"2014","dateType":"Issued"}],"language":"en","types":{"ris":"THES","bibtex":"phdthesis","citeproc":"thesis","schemaOrg":"Thesis","resourceType":"Dissertation","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[],"descriptions":[{"description":"Audio reproduction technologies have underwent several revolutions from a purely mechanical, to electromagnetic, and into a digital process. These changes have resulted in steady improvements in the objective qualities of sound capture/playback on increasingly portable devices. However, most mobile playback devices remove important spatial-directional components of externalized sound which are natural to the subjective experience of human hearing. Fortunately, the missing spatial-directional parts can be integrated back into audio through a combination of computational methods and physical knowledge of how sound scatters off of the listener's anthropometry in the sound-field. The former employs signal processing techniques for rendering the sound-field. The latter employs approximations of the sound-field through the measurement of so-called Head-Related Impulse Responses/Transfer Functions (HRIRs/HRTFs). \n\nThis dissertation develops several numerical and machine learning algorithms for accelerating and personalizing spatial audio reproduction in light of available mobile computing power. First, spatial audio synthesis between a sound-source and sound-field requires fast convolution algorithms between the audio-stream and the HRIRs. We introduce a novel sparse decomposition algorithm for HRIRs based on non-negative matrix factorization that allows for faster time-domain convolution than frequency-domain fast-Fourier-transform variants. Second, the full sound-field over the spherical coordinate domain must be efficiently approximated from a finite collection of HRTFs. We develop a joint spatial-frequency covariance model for Gaussian process regression (GPR) and sparse-GPR methods that supports the fast interpolation and data fusion of HRTFs across multiple data-sets. Third, the direct measurement of HRTFs requires specialized equipment that is unsuited for widespread acquisition. We ``bootstrap'' the human ability to localize sound in listening tests with Gaussian process active-learning techniques over graphical user interfaces that allows the listener to infer his/her own HRTFs. Experiments are conducted on publicly available HRTF datasets and human listeners.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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