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This paper considers neural signal processing applied to extracellular recordings, in particular, unsupervised action potential detection at a low signal-to-noise ratio. It adopts the basic framework of the multiresolution Teager energy operator (MTEO) detector, but presents important new results including a significantly improved MTEO detector with some mathematical analyses, a new alignment technique with its effects on the whole spike sorting system, and a variety of experimental results. Specifically, the new MTEO detector employs smoothing windows normalized by noise power derived from mathematical analyses and has an improved complexity by utilizing the sampling rate. Experimental results prove that this detector achieves higher detection ratios at a fixed false alarm ratio than the TEO detector and the discrete wavelet transform detector. We also propose a method that improves the action potential alignment performance. Observing that the extreme points of the MTEO output are more robust to the background noise than those of the action potentials, we use the MTEO output for action potential alignment. This brings not only noticeable improvement in alignment performance but also quite favorable influence over the classification performance. Accordingly, the proposed detector improves the performance of the whole spike sorting system. We verified the improvement using various modeled neural signals and some real neural recordings.