{"id":7071,"date":"2012-01-30T22:56:51","date_gmt":"2012-01-30T20:56:51","guid":{"rendered":"http:\/\/hgpu.org\/?p=7071"},"modified":"2012-01-30T22:56:51","modified_gmt":"2012-01-30T20:56:51","slug":"on-cuda-implementation-of-a-multichannel-room-impulse-response-reshaping-algorithm-based-on-p-norm-optimization","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7071","title":{"rendered":"On CUDA implementation of a multichannel room impulse response reshaping algorithm based on p-norm optimization"},"content":{"rendered":"<p>By using room impulse response shortening and shaping it is possible to reduce the reverberation effects and therefore improve speech intelligibility. This may be achieved by a prefilter that modifies the overall impulse response to have a stronger attenuation. For achieving a spatial robustness, multichannel approaches have been proposed. Unfortunately, these approaches suffer from a very high computational cost and are far too slow for being of practical use in applications where filters have to be designed in real-time. In this work we tackle this drawback using a CUDA implementation and achieve a speedup of over 130 times.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>By using room impulse response shortening and shaping it is possible to reduce the reverberation effects and therefore improve speech intelligibility. This may be achieved by a prefilter that modifies the overall impulse response to have a stronger attenuation. For achieving a spatial robustness, multichannel approaches have been proposed. Unfortunately, these approaches suffer from a [&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":[36,11,89,3],"tags":[849,1787,1782,14,20,298,931],"class_list":["post-7071","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-acoustics","tag-algorithms","tag-computer-science","tag-cuda","tag-nvidia","tag-optimization","tag-tesla-m2050"],"views":2303,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7071","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=7071"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7071\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7071"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7071"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7071"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}