Phenology is a key field of ecological research that examines the timing and seasonal dynamics of biological activities. However, conventional avian phenology studies have often relied on event-based metrics such as first arrival dates or first detection dates, which can be strongly influenced by sampling effort and chance observations. In this study, we proposed a cumulative quantile-based approach for quantifying avian vocal phenology using passive acoustic monitoring data and evaluated its applicability. We used vocalization data of the Fairy Pitta (Pitta nympha) collected from 36 study sites across South Korea in 2019 using automated recording units (Song Meter SM4). Hourly detection records were aggregated into daily vocal detections and used to construct cumulative vocal activity curves. The dates at which cumulative seasonal vocal activity reached 25%, 50%, and 75% were defined as T25, T50, and T75, respectively. We further compared these metrics with a conventional period-based quartile approach and evaluated their robustness through simulations of early and late outlier detections as well as missing-data scenarios. The estimated T25, T50, and T75 occurred on 26 May, 4 June, and 16 June 2019, respectively, indicating that 50% of the total seasonal vocal activity was concentrated within a 21-day period. In contrast, the period-based quartile method produced estimates that were 9, 21, and 30 days later, respectively. While first and last detection dates shifted by up to 9 and 14 days under outlier scenarios, T25, T50, and T75 remained unchanged. Under missing-data simulations, mean shifts remained below two days even when up to 30% of observations were removed. Under the data and validation conditions examined for the Fairy Pitta (Pitta nympha) in this study, the proposed cumulative quantile-based approach provided a relatively robust means of quantifying vocal phenology by incorporating the distribution of vocal activity across the entire season. With further validation across diverse species and long-term bioacoustic datasets, this approach may serve as a quantitative framework for phenological research.