{"id":41913,"date":"2026-07-29T07:27:12","date_gmt":"2026-07-29T05:27:12","guid":{"rendered":"https:\/\/www.graviton.at\/letterswaplibrary\/best-open-source-clean-speech-and-ambient-noise-datasets-for-training-an-edge-ai-audio-denoiser\/"},"modified":"2026-07-29T07:27:12","modified_gmt":"2026-07-29T05:27:12","slug":"best-open-source-clean-speech-and-ambient-noise-datasets-for-training-an-edge-ai-audio-denoiser","status":"publish","type":"post","link":"https:\/\/www.graviton.at\/letterswaplibrary\/best-open-source-clean-speech-and-ambient-noise-datasets-for-training-an-edge-ai-audio-denoiser\/","title":{"rendered":"Best Open-source Clean Speech And Ambient Noise Datasets For Training An Edge AI Audio Denoiser?"},"content":{"rendered":"<p><!-- SC_OFF --><\/p>\n<div class=\"md\">\n<p>We are building an edge-AI audio noise-reduction system on an ESP32-S3.<\/p>\n<p>Our architecture uses a lightweight GRUNet (~59k parameters) to output a dynamic gain mask on a 44-band Mel-spectrogram.<\/p>\n<p>\u200bI need gigabytes of audio to train the model. Does anyone have recommendations for the best open-source datasets for:<\/p>\n<p>1&gt; \u200bClean, isolated human speech.<\/p>\n<p>2&gt; \u200bDiverse ambient background noise (traffic, crowds, machinery, etc.).<\/p>\n<p>\u200bAlso, any tips or open-source scripts for artificially mixing these at different Signal-to-Noise Ratios (SNRs) before generating the 16kHz Mel-spectrograms would be hugely appreciated! <\/p>\n<\/div>\n<p><!-- SC_ON -->   submitted by   <a href=\"https:\/\/www.reddit.com\/user\/saikat_munshib\"> \/u\/saikat_munshib <\/a> <br \/> <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1v9mlv0\/best_opensource_clean_speech_and_ambient_noise\/\">[link]<\/a><\/span>   <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1v9mlv0\/best_opensource_clean_speech_and_ambient_noise\/\">[comments]<\/a><\/span><\/p><div class='watch-action'><div class='watch-position align-right'><div class='action-like'><a class='lbg-style1 like-41913 jlk' href='javascript:void(0)' data-task='like' data-post_id='41913' data-nonce='ddd07821da' rel='nofollow'><img class='wti-pixel' src='https:\/\/www.graviton.at\/letterswaplibrary\/wp-content\/plugins\/wti-like-post\/images\/pixel.gif' title='Like' \/><span class='lc-41913 lc'>0<\/span><\/a><\/div><\/div> <div class='status-41913 status align-right'><\/div><\/div><div class='wti-clear'><\/div>","protected":false},"excerpt":{"rendered":"<p>We are building an edge-AI audio noise-reduction system on an ESP32-S3. Our architecture uses a lightweight GRUNet&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[85],"tags":[],"class_list":["post-41913","post","type-post","status-publish","format-standard","hentry","category-datatards","wpcat-85-id"],"_links":{"self":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/41913","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/comments?post=41913"}],"version-history":[{"count":0,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/41913\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/media?parent=41913"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/categories?post=41913"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/tags?post=41913"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}