## 1 Introduction  
Cardiovascular diseases occupy first place among the causes of death around the world, according to the World Health Organization (WHO) [1]. Heart valve diseases (HVD) are also found in these figures, where moderate or severe valvular abnormalities are notably common in the adult population and increase their presence as the individuals age [2]. To examine the condition of the heart valves, the most common medical practice is auscultation, which consists of listening to acoustic characteristics directly via the patient's chest wall using a stethoscope. These heart sounds could be interpreted as the acoustic expression of the opening and closing of the four heart valves--tricuspid, mitral, pulmonary and aortic--where the muscular contraction that drives the blood from one cavity to another generates a high acceleration and delay of the blood flow, causing a pressure difference [3, 4]. Its normal physiological functioning is always unidirectional, which allows the correct circulation of blood through the cardiovascular circuit. However, some sounds are produced by the interruption of laminar blood flow by turning into turbulent flow, which is explained by abnormal and pathological functioning of the heart valves. The cardiac cycle is composed of two phases: the systole, during which the ventricles contract and drive blood to the blood vessels, and the diastole, in which the ventricles are filled.The systole begins with the closure of the mitral and triscuspid valves, producing the first heart sound or S1, while the diastole starts with the closure of the aortic and pulmonic valves, producing the second heart sound or S2. In addition to S1 and S2, extra sounds could be present during the cardiac cycle, which may indicate an abnormality [5, 6]. The duration of the noise varies depending on the valvular abnormality [7]. Figure 1 illustratesthe approximate duration of the heart sounds present in a normal cardiac cycle and heart noises in different valvular abnormalities. Since the range of frequencies that heart sounds generate are near the lowest threshold of sensitivity for the human ear, a practising physician requires extensive training guided by experienced physicians to make a correct diagnosis. In an alternative scenario, heart sounds can be recorded using electronic stethoscopes, allowing the practitioner to listen to heart sounds as many times as necessary to train the ear. This has proven effective in improving physicians' skills without the need to rely on available patients during hospital rotation [8]. In both cases, the diagnosis will depend fundamentally on the skill of the physician, subject to human error. To alleviate the need for the correct training of the practising physician and to generate tools that can be used as auxiliary techniques in the diagnosis of heart valve diseases, in the present work, we implement a deep learning algorithm for the classification of common valvular diseases based on their temporal and spectral features. The dataset used contains phonocardiographic (PCG) records previously labelled in five classes: normal (N), aortic stenosis (AS), mitral regurgitation (MR), mitral stenosis (MS) and mitral valve prolapse (MVP). It is worth mentioning that the previously described dataset has already been used to address similar classification problems. Firstly, the authors of the database, in 2018 [9], proposed to perform a multiclass classification comparing the performance of Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and a Deep Neural Network (DNN), and the extracted features were Discrete Wavelet Transform (DWT) and Mel Frequency Cepstral Coefficients (MFCC).