Our next audio workshop could include a repair comparison. The aim is to help participants describe what they hear before they decide which version they prefer, rather than treating every change as an improvement. The source would be a generated music excerpt. We would use the same passage in every comparison. A page discussing ai music artifact removal gave me a starting point for the topic, but it does not settle how to structure the exercise. The versions should be presented at comparable listening levels. I could collect short descriptions before revealing the version names. An unprocessed reference belongs alongside any proposed repair. The worksheet could distinguish distracting texture from lost musical detail. We could repeat the excerpts in a different order and discuss disagreements, since a single preference vote would reveal little about the reasons behind it. Which controls would help participants make a fair comparison?

Our next audio workshop could include a repair comparison. The aim is to help participants describe what they hear before they decide which version they prefer, rather than treating every change as an improvement. The source would be a generated music excerpt. We would use the same passage in every comparison. A page discussing ai music artifact removal gave me a starting point for the topic, but it does not settle how to structure the exercise. The versions should be presented at comparable listening levels. I could collect short descriptions before revealing the version names. An unprocessed reference belongs alongside any proposed repair. The worksheet could distinguish distracting texture from lost musical detail. We could repeat the excerpts in a different order and discuss disagreements, since a single preference vote would reveal little about the reasons behind it. Which controls would help participants make a fair comparison?