Fault diagnosis in high-speed machining centers (HSM) is critical in manufacturing systems, since early detection saves a substantial amount of time and money. It is known that 42% of failures in these centers occur in rotatory machineries, such as spindles, in which, the bearings are fundamental elements for effective operation. Nowadays, there are several machine- and deep-learning methods to diagnose the faults. To improve the performance of those traditional machine-learning tools, a deep-learning network that works on raw signals, which do not require previous analysis, has been proposed. The 1D Convolutional Neural Network (CNN) proposed model showed great capacity of adapting to three types of configurations and three different databases, despite a training set with a smaller number of categories. The network still detected faults at early damage stages. Additionally, the low computational cost shows the Deep-Learning Neural Network’s (DLNN) suitability for real-time applications in industry. The proposed structure reached a precision of 99%; real-time processing was around 8 ms per signal, and standard deviation of repeatability was 0.25%.

In this paper, the Mexican Emotional Speech Database (MESD) that contains single-word emotional utterances for anger, disgust, fear, happiness, neutral and sadness with adult (male and female) and child voices is described. To validate the emotional prosody of the uttered words, a cubic Support Vector Machines classifier was trained on the basis of prosodic, spectral and voice quality features for each case study: (1) male adult, (2) female adult and (3) child. In addition, cultural, semantic, and linguistic shaping of emotional expression was assessed by statistical analysis. This study was registered at BioMed Central and is part of the implementation of a published study protocol. Mean emotional classification accuracies yielded 93.3%, 89.4% and 83.3% for male, female and child utterances respectively. Statistical analysis emphasized the shaping of emotional prosodies by semantic and linguistic features. A cultural variation in emotional expression was highlighted by comparing the MESD with the INTERFACE for Castilian Spanish database. The MESD provides reliable content for linguistic emotional prosody shaped by the Mexican cultural environment. In order to facilitate further investigations, a corpus controlled for linguistic features and emotional semantics, as well as one containing words repeated across voices and emotions are provided. The MESD is made freely available.

Acoustics has been integrated with archaeology to better understand the social and cultural context of past cultures. Specifically, public events such as rituals or ceremonies, where an appreciation of sound propagation was required to hold an event. Various acoustic techniques have been used to study archaeological sites, providing information about the building characteristics and organizational structures of ancient civilizations. This review aims to present recent advances in Archaeoacoustics worldwide over the last seven years (2016–2022). For this purpose, one hundred and five articles were identified and categorized into two topics: (1) Archaeoacoustics in places, and (2) Archaeoacoustics of musical instruments and pieces. In the first topic, three subtopics were identified: (1) measurement and characterization of places, (2) rock art, and (3) simulation, auralization, and virtualization. Regarding the first subtopic, it was identified that the standards for reverberation times in enclosures are generally applied in their development. In the second subtopic, it was determined that the places selected to make paintings were areas with long reverberation time. The last subtopic, simulation, auralization, and virtualization, is the area of most remarkable growth and innovation. Finally, this review opens the debate to seek standardization of a measurement method that allows comparing results from different investigations.