{"id":2729,"date":"2024-05-20T22:20:00","date_gmt":"2024-05-20T22:20:00","guid":{"rendered":"https:\/\/coeng.uaa.alaska.edu\/innovation\/?p=2729"},"modified":"2024-12-20T22:24:34","modified_gmt":"2024-12-20T22:24:34","slug":"optimal-range-of-frequencies-for-estimating-tempo","status":"publish","type":"post","link":"https:\/\/coeng.uaa.alaska.edu\/innovation\/?p=2729","title":{"rendered":"Optimal Range of Frequencies for Estimating Tempo"},"content":{"rendered":"\n<p><strong>Student Members:<\/strong> Garrett Archer, Hiromi Kageyama<\/p>\n\n\n\n<p><strong>Faculty Advisors:<\/strong> Dr. Blair Kaneshiro, Dr. Masoumeh Heidari<\/p>\n\n\n\n<p><strong>Description:<\/strong><\/p>\n\n\n\n<p>The ability to accurately predict song tempo from a subject\u00e2\u20ac\u2122s electroencephalogram (EEG) data<br>plays a crucial role in understanding music perception and cognition. This project set out to<br>determine if this data could be used for accurate prediction without using machine learning<br>methods, which require training and testing data that may not be available, and which frequency<br>range is the most effective for this prediction. We used data recorded from 20 participants who<br>listened to a selection of 10 songs. We created a program in MatLab that optimized this data for<br>every song and plotted the most significant peaks for each of the three ranges that we had<br>chosen on a categorical scatter plot. The results were then compared to the actual tempo of<br>that song. For most songs we found this method to be quite accurate, especially in the 3-7 Hz<br>range, though a couple songs proved to have more difficult tempos to predict. This program not<br>only can find use in its current state for determining tempo from pre-recorded data, but future<br>developments could see it being adapted for real-time tempo estimation or for use in<br>EEG-based music composition.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/coeng.uaa.alaska.edu\/uploads\/AY24\/Spring2024\/CS&amp;E%20-%20temp%20estimation\/Music_Tempo_Estimation_Kageyama_Archer.pptx_anonymous.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">View Project<\/a><\/div>\n\n\n\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/coeng.uaa.alaska.edu\/uploads\/AY24\/Spring2024\/CS&amp;E%20-%20temp%20estimation\/Optimal%20Range%20of%20Frequencies%20Final%20Writeup_anonymous.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">View Report<\/a><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Student Members: Garrett Archer, Hiromi Kageyama Faculty Advisors: Dr. Blair Kaneshiro, Dr. Masoumeh Heidari Description: The ability to accurately predict song tempo from a subject\u00e2\u20ac\u2122s electroencephalogram (EEG) dataplays a crucial role in understanding music perception and cognition. This project set out todetermine if this data could be used for accurate prediction without using machine learningmethods, [&hellip;]<\/p>\n","protected":false},"author":8,"featured_media":2730,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[24],"tags":[498],"discipline":[298],"academic-year":[499,316],"class_list":["post-2729","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-capstone-project","tag-computer-science-and-engineering","discipline-computer-science-engineering","academic_year-499","academic_year-academic_year"],"_links":{"self":[{"href":"https:\/\/coeng.uaa.alaska.edu\/innovation\/index.php?rest_route=\/wp\/v2\/posts\/2729","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/coeng.uaa.alaska.edu\/innovation\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/coeng.uaa.alaska.edu\/innovation\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/coeng.uaa.alaska.edu\/innovation\/index.php?rest_route=\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/coeng.uaa.alaska.edu\/innovation\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2729"}],"version-history":[{"count":1,"href":"https:\/\/coeng.uaa.alaska.edu\/innovation\/index.php?rest_route=\/wp\/v2\/posts\/2729\/revisions"}],"predecessor-version":[{"id":2732,"href":"https:\/\/coeng.uaa.alaska.edu\/innovation\/index.php?rest_route=\/wp\/v2\/posts\/2729\/revisions\/2732"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/coeng.uaa.alaska.edu\/innovation\/index.php?rest_route=\/wp\/v2\/media\/2730"}],"wp:attachment":[{"href":"https:\/\/coeng.uaa.alaska.edu\/innovation\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2729"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/coeng.uaa.alaska.edu\/innovation\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2729"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/coeng.uaa.alaska.edu\/innovation\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2729"},{"taxonomy":"discipline","embeddable":true,"href":"https:\/\/coeng.uaa.alaska.edu\/innovation\/index.php?rest_route=%2Fwp%2Fv2%2Fdiscipline&post=2729"},{"taxonomy":"academic_year","embeddable":true,"href":"https:\/\/coeng.uaa.alaska.edu\/innovation\/index.php?rest_route=%2Fwp%2Fv2%2Facademic-year&post=2729"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}