Finding Singing Talent through AI

What if we could find the next greatest soprano through AI and the capabilities of Speech Models of AI?

Grammy-nominated producer Alex da Kid used IBM Watson to analyze five years’ of hit songs, as well as cultural data from films, social media, and online articles to figure out a theme for an AI-generated song that fans would enjoy. The final song, “Not Easy,” reached number four on the iTunes Hot Tracks chart within 48 hours of its release.

The novel idea I propose is to find great singing talents through data assistants Alexa and Siri etc. Once data is there it should be matched with the voice types we know belong to great singing talents/musicals. Here is how how it ideally looks like.

Vocal range is the range of pitches that a human voice can phonate. A common application is within the context of singing, where it is used as a defining characteristic for classifying singing voices into voice types.

Women are typically divided into three main groups: soprano, mezzo-soprano, and contralto. Men are usually divided into four main groups: countertenor, tenor, baritone, and bass. When considering the pre-pubescent voices of children an eighth term, treble can be applied.

Within each of these major categories, there are several sub-categories that identify specific vocal qualities like coloratura facility and vocal weight to differentiate between voices.

Scientific pitch notation (SPN), also known as American standard pitch notation (ASPN) and international pitch notation (IPN), is a method of specifying musical pitch by combining a musical note name (with accidental if needed) and a number identifying the pitch’s octave.

Now gathering data worldwide through voice assistants and grouping them by their pitch octave can lead us to find the next Mariah Carey and Ariana Grande. So what should we call the show AI’s got talent?

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