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Purpose: Respiratory-event rates quantify event frequency but do not describe how respiratory-related acoustic activity is expressed across an overnight recording. We investigated whether a score-defined digital acoustic representation captures information not reducible to annotated respiratory-event rate.Methods: We analyzed 32 overnight home respiratory polygraphy recordings from the APSAA dataset, including audio, respiratory annotations, and oxygen saturation. Twenty-eight recordings yielded at least one qualifying acoustic episode (8,512 episodes). Five acoustic features—episode density, relative intensity, duration, long-event occurrence, and temporal distribution—formed a composite score defining three acoustic phenotypes using fixed heuristic thresholds. Temporal compaction quantified short inter-episode intervals and was adjusted for episode quantity.Results: Scores were computationally reproduced with identical phenotype assignment in 28/28 recordings (mean absolute difference 0.0011; maximum 0.0046). Two recordings with comparable annotation-derived respiratory-event rates (30.45 vs 33.38 events/h; 217 vs 232 annotated events) and mean SpO₂ (91.75% vs 91.61%) contained 382 vs 5 acoustic episodes and scores of 0.685 vs 0.145. Load-adjusted temporal compaction differed among phenotypes (H=8.192, p=0.0166, ε²=0.248). The effect persisted after removing the historical 120-s interval exclusion (H=8.108, p=0.0174, ε²=0.244); residual association with episode count remained. The low- versus intermediate-score contrast was robust, whereas the high-score contrast was less stable.Conclusions: Overnight respiratory audio can provide reproducible digital acoustic phenotypes describing the amount and temporal organization of detected acoustic activity beyond annotated event frequency. These are signal representations, not clinical phenotypes or sleep stages. Repeated-night studies are required to establish within-person stability, transitions, and clinical meaning.