Acoustic emission (A E) has been studied as a potential information source for machine fault diagnosis for a long time. However, AE sensors have not yet been applied widely in real applications. Firstly, in comparison with other sensors such as vibration, AE sensors require much higher sampling rate. The characteristic frequency of AE signals generally falls into the range of 100 kHz to several MHz, which requires a sampling system with at least 5MHz sampling rate. Secondly, the storage and computational burden for large volume of AE data is tremendous. Thirdly, AE signal generally contains certain non-stationary behaviors which make traditional frequency analysis ineffective. In this paper, a frequency reduction technique and a modified time synchronous average (TSA) based signal processing method are proposed to identify gear fault using AE signals. Heterodyne technique commonly used in communication is employed to preprocess the AE signals before sampling. By heterodyning, the AE signal frequency is down shifted from several hundred kHz to below 50 kHz. Then a low sampling rate comparable to that of vibration sensors could be applied to sample the AE signals. After that, a modified tachometer less TSA method is adopted to further analyze the AE signal feature. Instead of performing TSA on the raw signals, the time synchronous averaging of the first order harmonic signal is obtained and analyzed. With the presented method, no tachometer or real time phase reference signal is required. The TSA reference signal is directly obtained from AE signals. By examining the smoothness of obtained wave form, a noticeable discontinuity or irregularity could be easily observed for gear fault diagnosis. AE data collected from seeded fault tests on a gearbox are used to validate the proposed method. The analysis results of the tests have shown that the proposed method could reliably and accurately detect the tooth fault.