{"id":758989,"date":"2021-07-07T13:59:33","date_gmt":"2021-07-07T20:59:33","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=758989"},"modified":"2021-07-08T12:02:35","modified_gmt":"2021-07-08T19:02:35","slug":"supervised-deep-hashing-for-efficient-audio-event-retrieval","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/supervised-deep-hashing-for-efficient-audio-event-retrieval\/","title":{"rendered":"Supervised Deep Hashing for Efficient Audio Event Retrieval"},"content":{"rendered":"<p>Efficient retrieval of audio events can facilitate real-time implementation of numerous query and search-based systems. This work investigates the potency of different hashing techniques for efficient audio event retrieval. Multiple state-of-the-art weak audio embeddings are employed for this purpose. The performance of four classical unsupervised hashing algorithms is explored as part of off-the-shelf analysis. Then, we propose a partially supervised deep hashing framework that transforms the weak embeddings into a low-dimensional space while optimizing for efficient hash codes. The model uses only a fraction of the available labels and is shown here to significantly improve the retrieval accuracy on two widely employed audio event datasets. The extensive analysis and comparison between supervised and unsupervised hashing methods presented here, give insights on the quantizability of audio embeddings. This work provides a first look in efficient audio event retrieval systems and hopes to set baselines for future research.<\/p>\n<div id=\"attachment_759010\" style=\"width: 1606px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-759010\" class=\"size-full wp-image-759010\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/07\/Capture.jpg\" alt=\"deep audio event retrieval\" width=\"1596\" height=\"322\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/07\/Capture.jpg 1596w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/07\/Capture-300x61.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/07\/Capture-1024x207.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/07\/Capture-768x155.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/07\/Capture-1536x310.jpg 1536w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/07\/Capture-16x3.jpg 16w\" sizes=\"(max-width: 1596px) 100vw, 1596px\" \/><p id=\"caption-attachment-759010\" class=\"wp-caption-text\">Fig. 1: Overview of the employed model for deep audio event hashing<\/p><\/div>\n<p>&nbsp;<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-759019\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/07\/Capture2.jpg\" alt=\"map plots audio retrieval\" width=\"1538\" height=\"563\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/07\/Capture2.jpg 1538w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/07\/Capture2-300x110.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/07\/Capture2-1024x375.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/07\/Capture2-768x281.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/07\/Capture2-1536x562.jpg 1536w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/07\/Capture2-16x6.jpg 16w\" sizes=\"(max-width: 1538px) 100vw, 1538px\" \/><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Efficient retrieval of audio events can facilitate real-time implementation of numerous query and search-based systems. This work investigates the potency of different hashing techniques for efficient audio event retrieval. Multiple state-of-the-art weak audio embeddings are employed for this purpose. The performance of four classical unsupervised hashing algorithms is explored as part of off-the-shelf analysis. Then, [&hellip;]<\/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":[243062],"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":[246694,246691,257593,252745,246685,248869],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-758989","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-audio-acoustics","msr-locale-en_us","msr-field-of-study-artificial-intelligence","msr-field-of-study-computer-science","msr-field-of-study-event-retrieval","msr-field-of-study-hash-function","msr-field-of-study-machine-learning","msr-field-of-study-quantization-signal-processing"],"msr_publishername":"IEEE","msr_edition":"","msr_affiliation":"","msr_published_date":"2020-5-3","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":"IEEE","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":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/07\/ICASSP2020_Efficient_Audio_Retrieval1.pdf","label_id":"243132","label":0},{"type":"doi","viewUrl":"false","id":"false","title":"10.1109\/ICASSP40776.2020.9053766","label_id":"243106","label":0}],"msr_related_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/07\/ICASSP2020_Efficient_Audio_Retrieval1.pdf","id":"759016","title":"icassp2020_efficient_audio_retrieval1","label_id":"243112","label":0},{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/www.microsoft.com\/en-us\/research\/video\/supervised-deep-hashing-for-efficient-audio-retrieval\/","label_id":"243118","label":0}],"msr_attachments":[{"id":759016,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/07\/ICASSP2020_Efficient_Audio_Retrieval1.pdf"}],"msr-author-ordering":[{"type":"text","value":"Arindam Jati","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Dimitra Emmanouilidou","user_id":37461,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Dimitra Emmanouilidou"}],"msr_impact_theme":[],"msr_research_lab":[199565],"msr_event":[],"msr_group":[144923],"msr_project":[559086],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":559086,"post_title":"Audio Analytics","post_name":"audio-analytics","post_type":"msr-project","post_date":"2019-02-08 15:57:54","post_modified":"2023-01-13 13:28:08","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/audio-analytics\/","post_excerpt":"Audio analytics is about analyzing and understanding audio signals captured by digital devices, with numerous applications in enterprise, healthcare, productivity, and smart 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