{"id":10330,"date":"2025-01-23T10:02:04","date_gmt":"2025-01-23T08:02:04","guid":{"rendered":"https:\/\/aire-edih.eu\/?post_type=project&#038;p=10330"},"modified":"2025-11-03T10:46:59","modified_gmt":"2025-11-03T08:46:59","slug":"2024-testing-ai-algorithms-for-the-automatic-extraction-and-application-of-guitar-effects-to-enable-new-product-lines","status":"publish","type":"project","link":"https:\/\/aire-edih.eu\/en\/project\/2024-testing-ai-algorithms-for-the-automatic-extraction-and-application-of-guitar-effects-to-enable-new-product-lines\/","title":{"rendered":"2024: Testing AI algorithms for the automatic extraction and application of guitar effects to enable new product lines"},"content":{"rendered":"<p><span class=\"TextRun SCXW187434855 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW187434855 BCX0\">Recreating guitar tones from existing music is a time-consuming and technically <\/span><span class=\"NormalTextRun SCXW187434855 BCX0\">difficult task<\/span><span class=\"NormalTextRun SCXW187434855 BCX0\"> for musicians. The goal of this project was to test and <\/span><span class=\"NormalTextRun SCXW187434855 BCX0\">validate<\/span><span class=\"NormalTextRun SCXW187434855 BCX0\"> AI-based methods for automatically extracting guitar effects from songs and applying them to new performances. The system was divided into three functional blocks, each <\/span><span class=\"NormalTextRun SCXW187434855 BCX0\">validated<\/span><span class=\"NormalTextRun SCXW187434855 BCX0\"> with different neural architectures. For source separation (Block 1), UVR and Moises.AI models gave promising results in isolating guitar tracks. For clean signal reconstruction (Block 2), a combination of Mel-Denoiser and HiFi-GAN showed potential but <\/span><span class=\"NormalTextRun SCXW187434855 BCX0\">remains<\/span><span class=\"NormalTextRun SCXW187434855 BCX0\"> the key bottleneck. In Block 3, GCNTF was <\/span><span class=\"NormalTextRun SCXW187434855 BCX0\">validated<\/span><span class=\"NormalTextRun SCXW187434855 BCX0\"> for modeling and reapplying effects to new guitar inputs. A web interface was adjusted for testing, and a command-line tool was developed for delay modeling. The project successfully <\/span><span class=\"NormalTextRun SCXW187434855 BCX0\">demonstrated<\/span><span class=\"NormalTextRun SCXW187434855 BCX0\"> the feasibility of the core concept and <\/span><span class=\"NormalTextRun SCXW187434855 BCX0\">identified<\/span><span class=\"NormalTextRun SCXW187434855 BCX0\"> key areas for improvement. This work lays the foundation for future development toward real-time, AI-driven guitar tools.<\/span><\/span><span class=\"EOP SCXW187434855 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;469777462&quot;:[1347],&quot;469777927&quot;:[0],&quot;469777928&quot;:[1]}\">\u00a0<\/span><\/p>\n<div  class='avia-button-wrap av-av_button-5b5d4df47d8425147c9b35e9017b300f-wrap avia-button-left '><a href='https:\/\/github.com\/ai-robotics-estonia\/Testing_AI_algorithms_for_automatic_extraction_and_application_of_guitar_effects_to_enable_products' class='avia-button av-av_button-5b5d4df47d8425147c9b35e9017b300f avia-icon_select-no avia-size-small avia-position-left avia-color-dark' target=\"_blank\" rel=\"noopener noreferrer\"><span class='avia_iconbox_title' >Github<\/span><\/a><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Recreating guitar tones from existing music is a time-consuming and technically difficult task for musicians. The goal of this project was to test and validate AI-based methods for automatically extracting [&hellip;]<\/p>\n","protected":false},"featured_media":10649,"template":"","year_tax":[86],"company_tax":[196],"status_tax":[92],"university_tax":[90],"sector":[168],"project_cat":[65],"class_list":["post-10330","project","type-project","status-publish","has-post-thumbnail","hentry","year_tax-2024-en","company_tax-noise-machine-ou-en","status_tax-completed-projects","university_tax-taltech-en","sector-production-process","project_cat-completed-projects"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AIRE - 2024: Testing AI algorithms for the automatic extraction and application of guitar effects to enable new product lines<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/aire-edih.eu\/en\/project\/2024-testing-ai-algorithms-for-the-automatic-extraction-and-application-of-guitar-effects-to-enable-new-product-lines\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AIRE - 2024: Testing AI algorithms for the automatic extraction and application of guitar effects to enable new product lines\" \/>\n<meta property=\"og:description\" content=\"Recreating guitar tones from existing music is a time-consuming and technically difficult task for musicians. 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