The statistical model for this experiment is known, in some quarters, as a random coefficients model (essentially, regression in a mixed model).
The current model assumes that the relationship between Avds_recover and PCAVDS_base is linear, and that the slope is the same regardless of sound or condition (because the model does not include interactions of sound and/or condition with PCAVDS_base). Are these assumptions valid?
IF I understand your study design correctly, then I would consider
proc mixed;
class subject sound condition;
model Avds_recover = PCAVDS_base | sound | condition;
random subject(condition);
random intercept PCAVDS_base / subject=sound*subject(condition);
lsmeans sound | condition;
run;
Generally, REML is preferable to ML. Assumptions include normality, homogeneity of variance, and linearity. Also that order of sound presentation doesn't matter--that a subsequent trial is not affected by previous trials. You don't say how sound level was assigned to trials.
Trial would never be a SUBJECT in a RANDOM statement (if I'm interpreting trial correctly). Sound probably should not be in a RANDOM statement with TYPE=UN; it would be better dealt with in a separate RANDOM statement as appropriate.
There are a variety of ways to specify the random structure for the model. Differences in the specification of SUBJECT= and optionally GROUP= produce different covariance structures. Theoretically, one will fit the data best. You definitely want to know what you're doing with different specifications. It's complicated (no surprise there).
Personally, I don't see the need for a REPEATED statement in a model like this, but some people include one to deal with the temporal autocorrelation among trials. If you include REPEATED, I doubt you can have two "group=" (although I've never tried), and you would need to either have the data set sorted appropriately by trial (which is the repeated measure) or, better yet, include trial (as REPEATED trial / ...).
I'll make my usual recommendation: read
https://www.sas.com/store/books/categories/usage-and-reference/sas-for-mixed-models-second-edition/prodBK_59882_en.html
This is a challenging model and you really don't want to go about it blindly.
If you haven't already done so, plot your data.
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