Speech emotion recognition (SER) is widely applicable today, benefiting areas such as entertainment, robotics, and healthcare. This emotional understanding enhances user-machine interaction, making systems more responsive and providing more natural experiences. In robotics, SER is useful in home assistance devices, eldercare, and special education, facilitating effective communication. Additionally, in healthcare settings, it can monitor patients’ emotional well-being. However, achieving high levels of accuracy is challenging and complicated by the need to select the best combination of machine learning algorithms, hyperparameters, datasets, data augmentation, and feature extraction methods. Therefore, this study aims to develop a deep learning approach for optimal SER configurations. It delves into the domains of optimizer settings, learning rates, data augmentation techniques, feature extraction methods, and neural architectures for the RAVDESS, TESS, SAVEE, and R+T+S (RAVDESS+TESS+SAVEE) datasets. After finding the best SER configurations, meta-learning is carried out, transferring the best configurations to two additional datasets, CREMA-D and R+T+S+C (RAVDESS+TESS+SAVEE+CREMA-D). The developed approach proved effective in finding the best configurations, achieving an accuracy of 97.01% for RAVDESS, 100% for TESS, 90.62% for SAVEE, and 97.37% for R+T+S. Furthermore, using meta-learning, the CREMA-D and R+T+S+C datasets achieved accuracies of 83.28% and 90.94%, respectively.

Artificial intelligence has significant applications in computer vision studies for cultural heritage. In this research field, visual inspection of historical buildings and the digitization of heritage using machine learning models stand out. However, the literature still lacks datasets for the classification and identification of Brazilian religious buildings using deep learning, particularly with images from the historic town of Ouro Preto. It is noteworthy that Ouro Preto was the first Brazilian World Heritage Site recognized by UNESCO in 1980. In this context, this paper aims to address this gap by proposing a new image dataset, termed ImageOP: The Image Dataset with Religious Buildings in the World Heritage Town of Ouro Preto for Deep Learning Classification. This new dataset comprises 1613 images of facades from 32 religious monuments in the historic town of Ouro Preto, categorized into five classes: fronton (pediment), door, window, tower, and church. The experiments to validate the ImageOP dataset were conducted in two stages: simulations and computer vision using smartphones. Furthermore, two deep learning structures (MobileNet V2 and EfficientNet B0) were evaluated using Edge Impulse software. MobileNet V2 and EfficientNet B0 are architectures of convolutional neural networks designed for computer vision applications aiming at low computational cost, real-time classification on mobile devices. The results indicated that the models utilizing EfficientNet achieved the best outcomes in the simulations, with accuracy = $94.5\%$, precision = $96.0\%$, recall = $96.0\%$, and F-score = $96.0\%$. Additionally, superior accuracy values were obtained in detecting the five classes: fronton ($96.4\%$), church ($97.1\%$), window ($89.2\%$), door ($94.7\%$), and tower ($95.4\%$). The results from the experiments with computer vision and smartphones reinforced the effectiveness of the proposed dataset, showing an average accuracy of $88.0\%$ in detecting building elements across nine religious monuments tested for real-time mobile device application. The dataset is available in the Mendeley Data repository.

Speech emotion recognition is a challenging and essential task with numerous applications in human-computer interaction, healthcare, and entertainment. However, achieving high accuracy in this task is complicated by the need to select the best combination of machine learning algorithms, databases, data augmentation techniques, and feature extraction methods. This paper discusses the difficulty of choosing appropriate combinations of these factors and proposes a methodology to address this challenge. The proposed method evaluates the performance of various combinations of databases, data augmentation techniques, and feature extraction methods to determine the most effective approach for speech emotion recognition. The paper also presents a convolutional neural network to classify the emotions of happiness, sadness, fear, anger, surprise, disgust, and neutral. The results showed that the optimal combination proposed, with 94% accuracy, uses the combined RAVDESS and TESS databases, using data augmentation with noise, stretch, and pitch, and using MFCC to extract the characteristics of the audios.