The　acoustic　emission　(AE)　technique　is　one　of　the　most　widely　used　in　the　field　of　structural　monitoring.　Its　popularity　mainly　stems　from　the　fact　that　it　belongs　to　the　category　of　non-destructive　techniques　(NDT)　and　allows　the　passive　monitoring　of　structures.　The　technique　employs　piezoelectric　sensors　to　measure　the　elastic　ultrasonic　wave　that　propagates　in　the　material　as　a　result　of　the　crack　formation＇s　abrupt　release　of　energy.　The　recorded　signal　can　be　investigated　to　obtain　information　about　the　source　crack,　its　position,　and　its　typology　(Mode　I,　Mode　II).　Over　the　years,　many　techniques　have　been　developed　for　the　localization,　characterization,　and　quantification　of　damage　from　the　study　of　acoustic　emission.　The　onset　time　of　the　signal　is　an　essential　information　item　to　be　derived　from　waveform　analysis.　This　information　combined　with　the　use　of　the　triangulation　technique　allows　for　the　identification　of　the　crack　location.　In　the　literature,　it　is　possible　to　find　many　methods　to　identify,　with　increasing　accuracy,　the　onset　time　of　the　P-wave.　Indeed,　the　precision　of　the　onset　time　detection　affects　the　accuracy　of　identifying　the　location　of　the　crack.　In　this　paper,　two　techniques　for　the　definition　of　the　onset　time　of　acoustic　emission　signals　are　presented.　The　first　method　is　based　on　the　Akaike　Information　Criterion　(AIC)　while　the　second　one　relies　on　the　use　of　artificial　intelligence　(AI).　A　recurrent　convolutional　neural　network　(R-CNN)　designed　for　sound　event　detection　(SED)　is　trained　on　three　different　datasets　composed　of　seismic　signals　and　acoustic　emission　signals　to　be　tested　on　a　real-world　acoustic　emission　dataset.　The　new　method　allows　taking　advantage　of　the　similarities　between　acoustic　emissions,　seismic　signals,　and　sound　signals,　enhancing　the　accuracy　in　determining　the　onset　time.