Deep learning approaches for speech emotion recognition: state of the art and research challenges

Speech emotion recognition (SER) systems identify emotions from the human voice in the areas of smart healthcare, driving a vehicle, call centers, automatic translation systems, and human-machine interaction. In the classical SER process, discriminative acoustic feature extraction is the most important and challenging step because discriminative features influence the classifier performance and decrease the computational time. Nonetheless, current handcrafted acoustic features suffer from limited capability and accuracy in constructing a SER system for real-time implementation. Therefore, to overcome the limitations of handcrafted features, in recent years, variety of deep learning techniques have been proposed and employed for automatic feature extraction in the field of emotion prediction from speech signals. However, to the best of our knowledge, there is no in-depth review study is available that critically appraises and summarizes the existing deep learning techniques with their strengths and weaknesses for SER. Hence, this study aims to present a comprehensive review of deep learning techniques, uniqueness, benefits and their limitations for SER. Moreover, this review study also presents speech processing techniques, performance measures and publicly available emotional speech databases. Furthermore, this review also discusses the significance of the findings of the primary studies. Finally, it also presents open research issues and challenges that need significant research efforts and enhancements in the field of SER systems.

     

The use of Bis-(3-triethoxysilylpropyl) tetrasulphane for surface modification of silica,ferrite and kenaf fiber filled natural rubber composites; comparison of aqueous solvent deposition,dry blend and integral blend methods

In this work, Bis-(3-triethoxysilylpropyl) tetrasulphane was employed for surface modification of silica, ferrite and kenaf fiber filled natural rubber composites using aqueous solvent deposition, dry blending and integral blend methods. The efficiency of each method and the preferred modification method for improving the mechanical performance of natural rubber composites was assessed. The appearance of the Fourier transform infrared spectroscopy peak around 1088 cm⁻¹ for all types of fillers provided evidence that silane interaction had occurred between the fillers and rubber and the formation of siloxane linkages were quantitatively determined by the crosslink density measurement. The surface treatment by dry method for silica and ferrite fillers showed significant improvement of tensile performance at approximately 67% and 34% compared to those with untreated fillers. For kenaf fiber-filled rubber composites, the surface treatment by aqueous solvent deposition showed the highest tensile improvement of 59% compared to the dry blending and integral blend method.     

Fourier transform infrared (FTIR) authentication and batch-to-batch consistency for different types of paints using benchtop and handheld FTIR spectrophotometers for oil and gas industry

Standardization of Fourier transform infrared (FTIR) fingerprint region for paints and assessment on the reproducibility using different spectrophotometers were investigated. While selective fingerprint regions may be confusing for technicians/analysts who are non-chemists, we attempt to generalize these regions (e.g., 1300–1000 cm⁻¹ for Epoxy part A and 1400–1000 cm⁻¹ for Epoxy part B) by choosing a universal region (2000–900 cm⁻¹) that works for different paints. Comparison result using a paired student t-test shows that the degree of similarity (r) values from the studied regions are not statistically different. The paint fails the screening analysis occasionally on-site when analyzed using handheld FTIR due to the higher level of noise that gives low r values (r < 0.900 ± 0.002). The same samples were analyzed using a benchtop FTIR and the r values are above 0.900 ± 0.002. While the screening may lead to a false rejection of the sample on-site, there could be occurrence of false acceptance. The on-site screening of EPZ part A with different formulations, for instance, shows that the r values over the entire IR spectrum are above 0.900 ± 0.002 when analyzed using handheld FTIR. After the samples were analyzed using the benchtop, the r values fall below 0.900 ± 0.002.     

Blending science and public policies for remediation of a degraded ecosystem: Jackfish Bay, north shore of Lake Superior, Ontario, Canada

The Jackfish Bay Remedial Action Plan is the first of Lake Superior's Areas of Concern (AOCs) to consider recognition as an Area in Recovery (AiR). As a result of a high degree of complexity and uncertainty, ecosystem recovery in Jackfish Bay has been determined using a combination of regulatory policies and scientific evidence and extensive public and expert-based decision making. As a result, the conceptualization of the AiR status in Jackfish Bay has been developed with the adaptive management and the ecosystem approach, which provide the basic principles of assessing, monitoring, and managing the Area of Concern. To determine the status of beneficial use impairments caused by effluent from the Terrace Bay Pulp Inc., three public advisory committees—an academic panel of experts, a government technical review committee, and the Jackfish Bay Public Area in Recovery Review Committee (PARRC)—reviewed relevant scientific data and documents, including peer-reviewed publications, to assess changes in pollution levels in Jackfish Bay and improvements to aquatic, biotic, and benthic environments of the bay. The public decision-making process concluded with recommendations by the PARRC to develop a systematic monitoring program so that the ecosystem recovery process in the bay could be assessed on a continued basis, leading to its eventual delisting as an AOC. The entire process provides an example of blending science and public policies for remediation of a degraded ecosystem on the Great Lakes.     

Analyzing the Effects of Hyperthreading on the Performance of Data Management Systems

As information processing applications take greater roles in our everyday life, database management systems (DBMSs) are growing in importance. DBMSs have traditionally exhibited poor cache performance and large memory footprints, therefore performing only at a fraction of their ideal execution and exhibiting low processor utilization. Previous research has studied the memory system of DBMSs on research-based simultaneous multithreading (SMT) processors. Recently, several differences have been noted between the real hyper-threaded architecture implemented by the Intel Pentium 4 and the earlier SMT research architectures. This paper characterizes the performance of a prototype open-source DBMS running TPC-equivalent benchmark queries on an Intel Pentium 4 Hyper-Threading processor. We use hardware counters provided by the Pentium 4 to evaluate the micro-architecture and study the memory system behavior of each query running on the DBMS. Our results show a performance improvement of up to 1.16 in TPC-C-equivalent and 1.26 in TPC-H-equivalent queries due to hyperthreading.     

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

Theoretical Investigation of Cubic BaVO<Subscript>3</Subscript> and LaVO<Subscript>3</Subscript> Perovskites via Tran–Blaha-Modified Becke–Johnson Exchange Potential Approach

The full potential linearized augmented plane wave method of density functional theory has been used to investigate the structural, electronic, magnetic and thermoelectric properties of cubic perovskites BaVO₃ and LaVO₃. The ferromagnetic ground state has been found to be stable by comparing the total energies of non-spin-polarized and spin-polarized calculations performed for optimized unit cells. For both compounds, the bond length and tolerance factor are also measured. From the band structures and density of states plots, it is found that both compounds are half-metallic. We found that the presence of V at the octahedral site of these perovskites develops exchange splitting through p-d hybridization, which results in a stable ferromagnetic state. The observed exchange splitting is further clarified from the magnetic moment, charge and spin of the anion and cations. Finally, we also presented the calculated thermoelectric properties of these materials, which show that half-metallic BaVO₃ and LaVO₃ materials are potential contenders for thermoelectric applications.     

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.     

Helicopter maintenance error analysis: Beyond the third order of the HFACS-ME

In this paper a statistical analysis of a sample of 58 helicopter maintenance-induced safety occurrences is conducted to study helicopter accidents and incidents’ survivability and the severity distribution of such occurrences. Analysis is also carried out to identify helicopter main and sub-systems mostly exposed to maintenance errors and to determine various types of such errors. Expected inherent relations between rotorcraft components affected and types of associated maintenance errors are investigated. Human factors-based triggers of these accidents and severe incidents are explored. The concept of Specific Failures (SFs) that immediately precede each of such occurrences is introduced for more detailed representation of the last breached individual and organizational safety barriers. Root causes of these safety occurrences were then sought utilizing the Human Factors Analysis and Classification System-Maintenance Extension (HFACS-ME) taxonomy with a refined focus on its third order categories’ list. The rotorcraft characteristics influencing individuals and organizational behaviours within Maintenance, Repair and Overhaul organizations (MROs) are discussed in the light of the root cause investigation results.

Relevance to industry

The study of human reliability within helicopter maintenance industry is waited to emphasise the understanding of causes and propagation mechanisms of maintainers' errors and their consequences on the overall aviation safety. Previous works often investigated maintenance errors and their roles in promoting aviation accidents of fixed-wing aircraft; this research is investigating the case of rotorcraft.     

Dynamic hybrid automatic repeat request (DHARQ) for WiMAX – Mobile multihop relay using adaptive power control

Wireless multihop network is currently attracting much attention as a new wireless broadband access technology due to numerous benefits. This work proposes a power control scheme for WiMAX multihop relay system. In contrast to existing power control and optimization approaches, our proposed method uses an adaptive Channel Quality Measurement for a relay station to reduce interferences to other mobile station (MS) or relay station (RS) within the same cell and hence increase the number of hops per link and consequently maximize the spatial reuse. The proposed power control is applied to a new dynamic HARQ algorithm for adaptive channel quality enhancement. Simulation results have indicated that the proposed approach achieves superior BER/PER performance enhancement in comparison to previous related works.