{"id":1705,"date":"2010-11-27T16:26:37","date_gmt":"2010-11-27T16:26:37","guid":{"rendered":"http:\/\/hgpu.org\/?p=1705"},"modified":"2010-11-27T16:26:37","modified_gmt":"2010-11-27T16:26:37","slug":"aurally-and-visually-enhanced-audio-search-with-soundtorch","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1705","title":{"rendered":"Aurally and visually enhanced audio search with soundtorch"},"content":{"rendered":"<p>Finding a specific or an artistically appropriate sound in a vast collection comprising thousands of audio files containing recordings of, say, footsteps, gunshots, and thunderclaps easily becomes a chore. To improve on this, we have developed an enhanced auditory and graphical zoomable user interface that leverages the human brain&#8217;s capability to single out sounds from a spatial mixture: The user shines a virtual flashlight onto an automatically created 2D arrangement of icons that represent sounds. All sounds within the light cone are played back in parallel through a surround sound system. A GPU-accelerated visualization facilitates identifying the icons on the screen with acoustic items in the dense cloud of sound. Test show that the user can pick the &#8220;right&#8221; sounds more quickly and\/or with more fun than with standard file-by-file auditioning.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Finding a specific or an artistically appropriate sound in a vast collection comprising thousands of audio files containing recordings of, say, footsteps, gunshots, and thunderclaps easily becomes a chore. To improve on this, we have developed an enhanced auditory and graphical zoomable user interface that leverages the human brain&#8217;s capability to single out sounds from [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[3,41],"tags":[1789],"class_list":["post-1705","post","type-post","status-publish","format-standard","hentry","category-paper","category-signal-processing","tag-signal-processing"],"views":1986,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1705","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1705"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1705\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1705"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1705"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1705"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}