Phonocardiogram　(PCG)　is　commonly　used　as　a　diagnostic　tool　in　ambulatory　monitoring　in　order　to　evaluate　cardiac　abnormalities　and　detect　cardiovascular　diseases.　Although　cardiac　auscultation　is　widely　used　for　evaluation　of　cardiac　function,　the　analysis　of　heart　sound　signals　mostly　depends　on　the　clinician’s　experience　and　skills.　There　is　growing　demand　for　automatic　and　objective　heart　sound　interpretation　techniques.　The　objective　of　this　study　is　to　develop　an　automatic　classification　method　for　anomaly　(binary　and　multi-class)　detection　of　PCG　recordings　without　any　segmentation.　A　deep　neural　network　(DNN)　model　is　used　on　the　raw　data　during　the　extraction　of　the　features　of　the　PCG　inputs.　Deep　feature　maps　obtained　from　hierarchically　placed　layers　in　DNN　are　fed　to　various　shallow　classifiers　for　the　anomaly　detection,　including　support　vector　classifier　(SVC),　k-nearest　neighbors　(KNN),　random　forest　(RF),　gradient　boosting　(GB)　classifier,　decision　tree　(DT)　classifier,　quadratic　discriminant　analysis　(QDA),　and　multi-layer　perception　(MLP).　Principal　component　analysis　(PCA)　technique　is　used　to　reduce　the　high　dimensions　of　feature　maps.Finally,　two　famous　heart　sound　databases,　namely　PhysioNet/Computing　in　Cardiology　(CinC)　Challenge　heart　sound　database　and　heart　valve　disease　(HVD)　database,　are　used　for　evaluation.　The　databases　are　significantly　different　in　terms　of　the　tools　used　for　data　acquisition,　clinical　protocols,　digital　storages　and　signal　qualities,　making　it　challenging　to　process　and　analyze.　By　using　the　10-fold　cross-validation　style,　experimental　results　demonstrate　that　the　proposed　deep　features　with　shallow　classifiers　yield　highest　performance　with　accuracy　of　99.61%　and　99.44%　for　binary　and　multi-class　classification　on　the　two　databases,　respectively.　The　results　indicate　that　our　method　is　effective　for　the　detection　of　abnormal　heart　sound　signals　and　outperforms　other　state-of-the-art　methods.　©　2023,　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.