In-situ measurements of process responses during powder bed fusion with laser beam of metals (PBF-LB/M) additive manufacturing are essential to monitor and control defect formation and improve product quality. Currently employed sensors are often unable to capture multiple responses together, affected by interferences, and insufficient to fully realize the transient nature of PBF-LB/M process. To address this issue, we developed a novel acousto-optic signal-based in-situ measurement system that can capture multiple signals. The interference in the signals due to the laser beam was uniquely analyzed and eliminated using statistical data analysis. The interference-free signals were found to provide insights into the formation of spatter and porosity. At high energy density, the spatter formation due to the liquid metal expulsion and powder ejection is the main factor affecting the porosity. A high energy density results in more splashing, and the porosity increases accordingly. The standard deviation of photovoltage signals and the energy distribution proportion estimated by wavelet decomposition showed a positive correlation with the spatter and porosity, respectively. The results showed that the processed signals can help in adjusting energy input to reduce spatter and porosity and improve part quality. In addition, a fast prediction of relative density based on the energy distribution characteristics of frequency zones was achieved using a back-propagation neural network with a correlation coefficient (R2) of 98.64%.