Nowadays, cardiovascular diseases (CVDs) become great threat to human’s lives aggressively. A good way to prevent the death cause of CVDs is early discovery and interposing. This leads the research on the heartbeat signal analysis area in order to provide a cost-effective and time-saving prognostic approach for those cardiac disease victims. Heart auscultation signal has served as an important primary symptom to identify CVDs for a long time. Heart sound (HS) analysis by auscultation and phonocardiogram (PCG), graphical interpretation of the acoustical waves of heart were routinely assessed by experienced physicians for diagnosing CVDs. However, over the years with the advent of echocardiogram (ECHO), electrocardiogram (ECG), photoplethysmogram (PPG), sphygmogram (SPG), etc., its importance as a diagnostic tool has considerably declined. But it is still one of the most primary physiological signals worthwhile to note that physicians still primarily analyze the auscultation of the heart to find signs of pathologic conditions before requesting for ECHO, ECG, etc. In this research, a scheme for Heart Sound Intelligent Interpretation using multi-resolution analysis (MRA)-envelogram cooperated mel-scale frequency cepstral coefficients (MFCC) feature extraction and dynamic time warping (DTW) classification is presented. MFCC algorithm which approximates closely to response of human auditory system is further applied to carry HS feature extraction. DTW is applied to measure the distance between two HS signals as classifier. Distances got through DTW can serve as an index of identifying pathological symptoms. MFCC and DTW are cooperated with envelogram segmentation. Classification accuracy is influenced greatly by the segmentation results, which makes HS segmentation approach as a decisive step. The innovative envelogram focusing on shape of signal waveform resists influence of murmurs and segments HS events without any additional assisting channels. However, there are high frequency noises between HS beats in some cases explored from the HS benchmark database, which makes HS segmentation failed due to the slope of HS envelope changes too frequently. The components of noise are unknown, and in each case the noises are different. Thus MRA algorithm is adapted to remove such noise from HS signal in this research work thus to implement the envelogram as an anti-noise HS segmentation. MRA also prevents the influence of unnecessary frequency band to improve the performance of MFCC so that the sensitivity and specificity of the scheme increase greatly. The outcomes of testing various HSs are presented and discussed concretely. It is observed that the MRA has improved the segmentation accuracy of HS signals by 23.2% and the pathological symptom hidden inside of HS can be efficiently detected by the proposed scheme. Obviously, such an explored advanced technology might be applied to the developing CVD diagnosis system through analysing HS and plays important role.