Heart sounds convey important information regarding potential heart diseases. Currently, heart sound classification attracts many researchers from the fields of telemedicine, digital signal processing, and machine learning—among others—mainly to identify cardiac pathology as quickly as possible. This article proposes chaogram as a new transform to convert heart sound signals to colour images. In the proposed approach, the output image is, therefore, the projection of the reconstructed phase space representation of the phonocardiogram (PCG) signal on three coordinate planes. This has two major benefits: (1) it makes possible to apply deep convolutional neural networks to heart sounds and (2) it is also possible to employ a transfer learning scheme by converting a heart sound signal to an image. The performance of the proposed approach was verified on the PhysioNet dataset. Due to the imbalanced data on this dataset, it is common to assess the results quality using the average of sensitivity and specificity, which is known as score, instead of accuracy. In this study, the best results were achieved using the $InceptionV3$ model, which achieved a score of 88.06%.

Due to the high level of precision and remarkable capabilities to solve the intricate problems in industry and academia, convolutional neural networks (CNNs) are presented. Speech emotion recognition is an interesting application for CNNs in the field of audio processing. In this paper, a speech emotion recognition system based on a 3D CNN is suggested to analyze and classify the emotions. In the proposed method, the three-dimensional reconstructed phase spaces of the speech signals were calculated. Then, emotion-related patterns formed in these spaces were converted into 3D tensors. Accordingly, a 3D CNN for speech emotion recognition applied to two datasets, EMO-DB and eNTERFACE05, using a speaker-independent technique achieved 90.40% and 82.20% accuracy, respectively. By employing gender recognition, the accuracy rates on EMO-DB increased to 94.42% and on eNTERFACE05 rose to 88.47%. Realization of the introduced 3D CNN on both Intel CPU and NVIDIA GPU is also explored. The results of the implemented 3D CNN without and with regard to gender recognition show that GPU-based running is faster for the EMO-DB and eNTERFACE05 datasets than CPU-based executions (using Python).

Speech emotion recognition (SER) is a challenging field of research that has attracted research during the last two decades. Successful performance of Deep Convolutional Neural Networks (DNNs) in various difficult pattern recognition problems motivates researchers to develop SER systems based on deep learning algorithms. The most essential requirement in training a deep model is the presence of a large-scale dataset. However, in many cases, such an amount of data is not available. Transfer learning approaches provide a practical solution to this problem. In this paper, we proposed an SER system based on AlexNet, the well-known deep model trained on the large-scale ImageNet dataset. In this way, the novel enriched spectrogram calculated based on the fusion of wide-band and narrow-band spectrograms is developed as a proper input for such a model. The proposed fused spectrogram benefited from both high temporal and spectral resolution. These images have been applied to the pre-trained AlexNet. All the experiments were performed on the popular Emo-DB, IEMOCAP, and eNTERFACE05 datasets based on 10-fold cross-validation and Leave-One-Speaker-Group-Out known as speaker-dependent and speaker-independent techniques, respectively. The proposed approach gains competent performance in contrast to other state-of-the-art methods.