The acoustic emission (AE) technology is growing in its applicability to bearing defect diagnosis. Several publications have shown its effectiveness for earlier detection of bearing defects than vibration analysis. In the latter instance, detection and monitoring of defects can be achieved through temporal statistical indicators, which can further be improved by application of denoising techniques. This paper investigates the application of temporal statistical indicators for AE detection of bearing defects on a purposely built test-rig and assesses the effectiveness of various denoising techniques in improving sensitivity to early defect detection. It is concluded that the denoising methods offer significant improvements in identifying defects with AE, especially the self-adaptive noise cancellation method.