Acoustic Emission (AE) is a non-destructive evaluation method that uses transient elastic waves produced by the sudden release of mechanical energy in a material or structure. This method generates multiple AE events during testing; therefore, it is important to develop parameters that capture the characteristics of each event (AE hit). The paper introduces new dimensionless parameters to characterize the waveform of AE signals: Earliness, Transitoriness, and Early Transitoriness. The study shows that these parameters provide an accurate description of AE waveforms, in some respects, better than traditional parameters, which makes them suitable for filtering with simple rules or in combination with machine-learning techniques. Two examples of the application of AE hit filtering from sedimentation and soil compression experiments are provided. In the sedimentation test analysis, the proposed parameters were used with K-means clustering to filter AE hits from outside the zone of interest and to calculate the rate of sedimentation. In the compression test of a sand sample under oedometric conditions, a simple filtering rule was applied to discriminate AE hits from unwanted sources and obtain a clear AE energy cumulative curve. In both cases, the dimensionless parameters have shown the capacity to discriminate between different AE sources and paths and the possibility of filtering hits from unwanted sources.