Soundscape ecology is a rising field in recent years as the effects of anthropogenic sound pollution are widely discussed. Nowadays, scientists are trying to find the best way to describe environmental health using quantitative acoustic measurements. In search of the best acoustic index/indices that can be used for real-time and long-term underwater acoustic monitoring, we tested five different acoustic indices for their effectiveness and suitability for distinguishing and differentiating various types of sounds. One dataset with anthropogenic noises (boat, ship, and diver noises), natural ambient sounds (wind, water turbulence, and reef background noises), and biotic sounds (damselfish Dascyllus reticulatus and snapping shrimps sounds) was analyzed using Raven Pro and R. Our results suggest that acoustic richness (AR) and acoustic complex index (ACI) are capable of separating sound types with the consistency of subjective impression. We also find a strong positive linear correlation between sound exposure level (SEL) and average power spectral density (PSD). The AR exhibits a polynomial relationship with the increase of SEL. Acoustic entropy (H) does not have a significant difference between the three types of sounds. These results agree with the previous studies that AR can be used for differentiating random noises and pure tones, and ACI is capable of quantifying sound complexity. / 2024-09-30T00:00:00Z