For this project, my initial idea was based around the concept of audio recognition. Many audio tracks are considered iconic, and can be identified even with the source heavily distorted or edited. For example, “We Will Rock You” boasts the unmistakable “stomp stomp clap” sequence throughout the entire song; if an individual were to simply stomp the sidewalk, even with the rest of the track removed and the sequence off-beat, the tune would be recognizable.
As such, my project focused upon finding the breaking point for iconic tunes through means of distortion, and creating a tool where a user can input a song to be distorted and have said “breaking point” numerically identified. The distortion method would need to be consistent, and as such the tool I developed uses a vinyl track to initially convolve the inserted audio track (a subtle change, but enough to begin the distortion process), and then allows the user to alter a degradation slider as they see fit to alter the track’s sampling-rate ratio. The level of degradation is actively updated and recorded, such that when the listener no longer recognizes the inserted track, the level of sampling-rate distortion can be recorded and tested against other tracks.
In order to provide an example, three iconic tunes were tested in the tool; the aforementioned “We Will Rock You”, the Pink Panther theme, and “Country Roads”. Both the original and distorted versions can be found below. Of the three, ” We Will Rock You” required the heaviest distortion, with a sampling-rate ratio of only 0.005 before the track was thoroughly disguised. Pink Panther required a 0.011, and Country Roads required a comparatively generous 0.016 before it was thoroughly distorted.
Although this tool’s measurement is intrinsically subjective (testing showed recognition differed between listeners), the tool helps measure how recognizable a track’s underlying audio cues are, and how much distortion is required to thoroughly disguise said cues.
https://drive.google.com/drive/u/1/folders/1sc-1M4PufMW2tQM1ZzN6QSgnHDCLiqw7