I've been envisioning a tool that uses an iterative blind approach, to provide a personalized playback EQ.
The user provides a familiar song
The tool generates a playlist with 5-6 randomized EQ variations, including original.
The user listens to each variation, all are unlabeled, and ranks them before confirming their preference.
Finally the the highest ranked EQ is revealed with along with parameters, which the app can retain as a new "original" for future tests.
This could be expanded and refined in many ways, but a basic implementation would be plenty useful for personal use I think. Later the tool could ideally be improved in areas of functionality, like loudness matching, better deterministic controls for EQ variations, averaging between multiple songs.
I'd appreciate any pointers on how to get started.
The user provides a familiar song
The tool generates a playlist with 5-6 randomized EQ variations, including original.
The user listens to each variation, all are unlabeled, and ranks them before confirming their preference.
Finally the the highest ranked EQ is revealed with along with parameters, which the app can retain as a new "original" for future tests.
This could be expanded and refined in many ways, but a basic implementation would be plenty useful for personal use I think. Later the tool could ideally be improved in areas of functionality, like loudness matching, better deterministic controls for EQ variations, averaging between multiple songs.
I'd appreciate any pointers on how to get started.
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