The damage source location of acoustic emission (AE) signals is studied in this investigation. Noises can influence the accuracy of calculation significantly as the compressive wave velocity in rock is very fast. A novel fractional power model is proposed for use in wavelet packet transform analysis to decompose, filter, and reconstruct AE signals. Analysis of uniaxial compression testing data on sandstone samples has revealed that the denoising scheme can result in a higher signal to noise ratio (SNR) and a lower root mean square error (RMSE) compared with the conventional soft and hard thresholding functions. The arrival time of AE signals needs to be determined to perform the time difference of arrival (TDOA) analysis. A combined maximum likelihood estimation and Akaike’s information criterion (MLE–AIC) approach is initially used to compute the arrival time. To locate the damage source, a combined least squares–particle swarm optimization (LSPSO) method is originally proposed, which can reduce the number of iterations compared to the conventional particle swarm optimization (PSO) analysis. Two techniques have been evaluated based on either a predefined wave velocity range or interval theory to improve the accuracy of damage source location. Simulated artificial signals and AE signals obtained from pencil-lead breakage tests and uniaxial compression tests are analyzed using the LSPSO algorithm. The comparison of located damage source obtained from the current approach and the PCI-2 AE system has demonstrated the efficacy of the proposed analysis scheme.