In account of its abilities to follow the damage progression, also at early stages, the acoustic emission (AE) analysis has become an attractive technique for machine condition monitoring. An AE analysis involves the detection of transients within the signals, which are called AE bursts. Traditional methods for AE burst detection are based on the definition of threshold values. When the machine under analysis works under variable operating conditions, threshold-based methods could lead to poor results due to the influence of these conditions on the AE generation. The present work compares the ability of three AE burst detection methods in a planetary gearbox working under different rotational speeds and loads. The results showed that performance could be significantly improved by using factors of the root mean square value as threshold values instead of fixed values. Among the evaluated methods, the method that includes demodulation and differentiation as a signal processing technique had the best performance overall.