{"id":13,"date":"2018-04-21T02:37:09","date_gmt":"2018-04-21T02:37:09","guid":{"rendered":"http:\/\/blogs.harvard.edu\/es25oliviaphillips\/?p=13"},"modified":"2018-04-21T02:37:09","modified_gmt":"2018-04-21T02:37:09","slug":"design-approach","status":"publish","type":"post","link":"https:\/\/archive.blogs.harvard.edu\/es25oliviaphillips\/2018\/04\/21\/design-approach\/","title":{"rendered":"Design Approach"},"content":{"rendered":"

Equations<\/h2>\n

I plan on using equations in order to determine the standard distribution of frequencies that form a particular formant. For example, if I analyze each member of my group singing a particular vowel, I can collect the data of each formant, and create a distribution to analyze what the mean frequency is for each formant of that vowel. Since men and women have slightly different formants, I will also likely need to calculate the differences to see if blend among women differs from blend among men.<\/p>\n

I will likely use these equations, which I’ve sources from\u00a0http:\/\/integral-table.com\/downloads\/stats.pdf:<\/p>\n

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Mean: \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u03bc\u00a0= (1\/n)\u2211xi<\/sub><\/p>\n

Std. Deviation: \u00a0 \u00a0 \u00a0 \u00a0 \u00a0sx<\/sub> = Sqrt[1\/(n-1)\u2211[(xi<\/sub> \u2212x bar)^2]<\/p>\n<\/div>\n<\/div>\n<\/div>\n

z-score: \u00a0 \u00a0 \u00a0 \u00a0 \u00a0z = (x\u2212\u03bc)\/\u03c3<\/p>\n

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Correlation: \u00a0 \u00a0 \u00a0 \u00a0 \u00a0r = 1\/(n-1)\u00a0\u2211(from i=1 to n) [(xi<\/sub> \u2212x bar)\/sx\u00a0]<\/sup><\/sub>[(yi<\/sub> \u2212y bar)\/sy<\/sub>\u00a0]<\/sup><\/sub><\/p>\n

 <\/p>\n

Tools<\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

Praat<\/h4>\n

In order to analyze the different frequencies present when a speaker says or sings a certain formant, I will be using a technology called Praat. Developed in Europe, this software allows a user to record a vocal sound and then analyze the different frequencies produced. I plan to utilize the function that creates a spectrogram and a waveform. It not only shows dark bands for different formants, but it also has the functionality to superimpose a line of best fit so the user can better visualize the formant’s change over time. There is also a feature that allows the user to break up the speech signal into phonemes, which creates readily understandable images to be presented with the data.<\/p>\n

Here’s a screenshot of analyzing a word in Praat:<\/p>\n

\"\"<\/p>\n

Audacity<\/h4>\n

I plan on further analyzing the data I collect using Audacity. Audacity also allows for more intricate audio editing than Praat, so I will likely use it for filtering and trying to change formants in order to fix the blend on audio segments.<\/p>\n

Python<\/h4>\n

Things are still in the works, but I hope to have a component of my project that takes in audio samples and returns which vowel is being produced. If that is too difficult or not applicable enough to my project, I hope to find another way to be able to utilize Python in the way I analyze formants and calculate blend, in order to create a user interface in which a user who knows nothing about formants or the science of waves can still analyze blend. This will likely be in a Jupyter notebook, since that is such a great user interface.<\/p>\n","protected":false},"excerpt":{"rendered":"

Equations I plan on using equations in order to determine the standard distribution of frequencies that form a particular formant. For example, if I analyze each member of my group singing a particular vowel, I can collect the data of … Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":9579,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-13","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/archive.blogs.harvard.edu\/es25oliviaphillips\/wp-json\/wp\/v2\/posts\/13","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/archive.blogs.harvard.edu\/es25oliviaphillips\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/archive.blogs.harvard.edu\/es25oliviaphillips\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/archive.blogs.harvard.edu\/es25oliviaphillips\/wp-json\/wp\/v2\/users\/9579"}],"replies":[{"embeddable":true,"href":"https:\/\/archive.blogs.harvard.edu\/es25oliviaphillips\/wp-json\/wp\/v2\/comments?post=13"}],"version-history":[{"count":2,"href":"https:\/\/archive.blogs.harvard.edu\/es25oliviaphillips\/wp-json\/wp\/v2\/posts\/13\/revisions"}],"predecessor-version":[{"id":16,"href":"https:\/\/archive.blogs.harvard.edu\/es25oliviaphillips\/wp-json\/wp\/v2\/posts\/13\/revisions\/16"}],"wp:attachment":[{"href":"https:\/\/archive.blogs.harvard.edu\/es25oliviaphillips\/wp-json\/wp\/v2\/media?parent=13"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/archive.blogs.harvard.edu\/es25oliviaphillips\/wp-json\/wp\/v2\/categories?post=13"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/archive.blogs.harvard.edu\/es25oliviaphillips\/wp-json\/wp\/v2\/tags?post=13"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}