{"id":309125,"date":"2016-10-19T17:28:19","date_gmt":"2016-10-20T00:28:19","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=309125"},"modified":"2019-04-09T23:45:18","modified_gmt":"2019-04-10T06:45:18","slug":"accelerated-wave-based-acoustic-simulation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/accelerated-wave-based-acoustic-simulation\/","title":{"rendered":"Accelerated Wave-based Acoustic Simulation\u00a0"},"content":{"rendered":"

We present an efficient technique to model sound propagation accurately in an arbitrary 3D scene by numerically integrating the wave equation. We show that by performing an offline modal analysis and using eigenvalues from a refined mesh, we can simulate sound propagation with reduced dispersion on a much coarser mesh, enabling accelerated computation. Since performing a modal analysis on the complete scene is usually not feasible, we present a domain decomposition approach to drastically shorten the pre-processing time. We introduce a simple, efficient and stable technique for handling the communication between the domain partitions. We validate the accuracy of our approach against cases with known analytical solutions. With our approach, we have observed up to an order of magnitude speedup compared to a standard finite-difference technique.<\/p>\n","protected":false},"excerpt":{"rendered":"

We present an efficient technique to model sound propagation accurately in an arbitrary 3D scene by numerically integrating the wave equation. We show that by performing an offline modal analysis and using eigenvalues from a refined mesh, we can simulate sound propagation with reduced dispersion on a much coarser mesh, enabling accelerated computation. Since performing […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13551],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-309125","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-graphics-and-multimedia","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"","msr_affiliation":"","msr_published_date":"2008-6-2","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"309131","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/10\/9.pdf","id":"309131","title":"9","label_id":"243132","label":0},{"type":"doi","viewUrl":"false","id":"false","title":"10.1145\/1364901.1364916","label_id":"243106","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"Nikunj Raghuvanshi","user_id":33106,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Nikunj Raghuvanshi"},{"type":"text","value":"Nico Galoppo","user_id":0,"rest_url":false},{"type":"text","value":"Ming C. 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