Mean Opinion Score Test Research Articles

SECap: Speech Emotion Captioning with Large Language Model

Speech emotions are crucial in human communication and are extensively used in fields like speech synthesis and natural language understanding. Most prior studies, such as speech emotion recognition, have categorized speech emotions into a fixed set of classes. Yet, emotions expressed in human speech are often complex, and categorizing them into predefined groups can be insufficient to adequately represent speech emotions. On the contrary, describing speech emotions directly by means of natural language may be a more effective approach. Regrettably, there are not many studies available that have focused on this direction. Therefore, this paper proposes a speech emotion captioning framework named SECap, aiming at effectively describing speech emotions using natural language. Owing to the impressive capabilities of large language models in language comprehension and text generation, SECap employs LLaMA as the text decoder to allow the production of coherent speech emotion captions. In addition, SECap leverages HuBERT as the audio encoder to extract general speech features and Q-Former as the Bridge-Net to provide LLaMA with emotion-related speech features. To accomplish this, Q-Former utilizes mutual information learning to disentangle emotion-related speech features and speech contents, while implementing contrastive learning to extract more emotion-related speech features. The results of objective and subjective evaluations demonstrate that: 1) the SECap framework outperforms the HTSAT-BART baseline in all objective evaluations; 2) SECap can generate high-quality speech emotion captions that attain performance on par with human annotators in subjective mean opinion score tests.

WhistleGAN for Biomimetic Underwater Acoustic Covert Communication

This paper proposes a whistle-generative adversarial network (WhistleGAN) that generates whistles for biomimetic underwater covert acoustic communication. The proposed method generates new whistles to maintain covertness by avoiding the repetitive use of the same whistles. Since the human ear perceives octave frequency such that low-frequency resolution is relatively larger than that of low frequencies, the proposed WhistleGAN uses mel filter banks to keep the fidelity in mimicking while reducing the complexity. The mean opinion score test verified that the whistles generated by the proposed method and the recorded real whistles have a similar score of 4.3, and the computer simulations proved that the bit error rate performance of the proposed method is the same as that of the real whistle.

The Optimization of the STOI Algorithm Parameters in Presence of the White Gaussian Noise (WGN)

The first part of the paper describes the Short Time Objective Intelligibility - STOI algorithm, which makes an objective evaluation of intelligibility, as well as an algorithm for estimating the optimal parameters of the STOI algorithm, N and . Second part of the paper, described an experiment that examines the intelligibility of the sentences formed from the Serbian Matrix Sentence Test - SMST base using: a). subjective MOS (Mean Opinion Score) test and b). objective test STOI algorithm. Subsequently, a comparative analysis of the results were made and it was determinate an analytical formula which connecting optimal pairs (N, ). Using the results of the MOS test as a reference, a mean absolute error of estimation of the STOI algorithm was determined.

Application of Fuzzy Inference System Takagi-Sugeno Methods for Diagnosis of Diseases and Pests of Madura Paddy Rice Based on Symptoms

Diseases and pests of lowland rice are one of the factors that can cause a decrease in rice yields. Therefore, it is necessary to have a diagnostic system to identify diseases and pests of paddy rice from an early age based on damage symptoms. The process of diagnosis requires expertise, knowledge, and experience from experts. Therefore, this research tries to build an expert system that can diagnose diseases and pests of paddy rice early by applying the Fuzzy Inference System Takagi with the Sugeno method. Fuzzy Inference System Takagi forms fuzzy sets using implication functions (rules). Rule composition is obtained from a data set of relationships between regulations, where the affirmation (defuzzification) and input from defuzzification is a constant or linear equation. The Sugeno method is used to diagnose diseases and pests of rice plants based on the symptoms experienced. This research aims to help plant pest control officers diagnose diseases and pests of paddy rice plants from the symptoms that attack the rice. The testing technique used is system accuracy testing and Mean Opinion Score (MOS) testing. The MOS test was carried out by involving 30 respondents consisting of 10 farmers and 20 extension workers, where 4.27 was obtained on a scale of 5 which was categorized into a good system. while testing the accuracy obtained from testing the system on two experts on diseases and pests of Madura paddy rice plants in 30 different cases has resulted in an accuracy rate of 86.66%. The expert system built in this study was able to diagnose 13 diseases and pests of Madura paddy based on the knowledge of two experts on 38 symptoms, and the plan was feasible to use and categorized into a good system.

Underwater Biomimetic Covert Acoustic Communications Mimicking Multiple Dolphin Whistles

This paper presents an underwater biomimetic covert acoustic communication system that achieves high covertness and a high data rate by mimicking dolphin group whistles. The proposed method uses combined time–frequency shift keying modulation with continuous varying carrier frequency modulation, which mitigates the interference between two overlapping multiple whistles while maintaining a high data rate. The data rate and bit error rate (BER) performance of the proposed method were compared with conventional underwater covert communication through an additive white Gaussian noise channel, a modeled underwater channel, and practical ocean experiments. For the covertness test, the similarity of the proposed multiple whistles was compared with the real dolphin group whistles using the mean opinion score test. As a result, the proposed method demonstrated a higher data rate, better BER performance, and large covertness to the real dolphin group whistles.

Media Pembelajaran Kimia menggunakan Mobile Learning berbasis Android

Chemistry is a relatively new material for Xth grade high school students, but many high school students still do not understand the basic concepts of chemistry and the lack of use of technology. The purpose of this research is to design and build learning media to make it easier for students to get to know chemistry using the Android platform. The method used in building this application is the SDLC (System Development Life Cycle) method, which starts by collecting data for system testing. Data collection was carried out around the Kalam Kudus Christian High School school environment in Pekanbaru with 40 students as research subjects. The system design is designed so that every high school student can use the application independently using a personal account. The tests used are black box testing and mean opinion score (MOS) testing. The result of black box testing is that the display or menu can run smoothly. while for MOS results, around 95% state that the application that was built gets very decent results from high school students. So that this application is used in accordance with the needs of high school students in helping to recognize basic chemical concepts.

WEB-BASED INFORMATION SYSTEM DESIGN OF INVENTORY AND COVID-19 VACCINE DISTRIBUTION IN BANYUMAS REGENCY

This research derived from inventory management and distribution of the COVID-19 vaccine which is still done manually at the Banyumas Regency Department of Health. The big amount of data that needed to be processed made it difficult for officers to organize the data, as a result there are several problems such as missing and damaged data. This system could make health facilities registered under the Banyumas Regency Department of Health to see the stock items and request the goods faster and easier. The built system has made data management more efficient and effective. Web-based Information System Design of Inventory And COVID-19 Vaccine Distribution In Banyumas Regency uses a waterfall model and qualitative methods. The waterfall model is used to facilitate the creation of this system because it has clear and sequential stages, the qualitative methods used in this study include observation, interviews, and literature studies. The system used PHP and HTML as the programming languages which are then supported by MySQL as the database. The tests carried out to test the system are Black Box Testing and Mean Opinion Score Testing. This research produces a system that can carry out the process of managing and processing inventory data and distribution, the test results show that the system runs as expected and succeeds in facilitating user work to be faster, more effective, and efficient.

SIBI (Sign System Indonesian Language) Text-to-3D Animation Translation Mobile Application

This research proposed a mobile application prototype to translate Indonesian text into SIBI (Sign System for the Indonesian Language) 3D gestures animation to bridge the communication gap between the deaf and the other. To communicate in sign language, the signer will use his/her hands and fingers to demonstrate the word gesture, and at the same time, his/her mouth will pronounce the word being expressed. Therefore, the proposed mobile application needs two animation generator components: the hand gesture and the lip movement generator. Hand gestures are made using a motion capture sensor. Mouth movements are created for all syllables available in the SIBI dictionary using the Dirichlet Free-Form Deformation (DFFD) method. The subsequent challenging work is synchronizing these two components and adding transitional gestures. A transitional gesture done by the cross-fading method is needed to make a word gesture that can smoothly connect with the next word gesture. The Mean Opinion Score (MOS) test was run to measure the mouth movements in 3D animation. The MOS score is 4.422. There are four surveys conducted to measure user satisfaction. The surveys showed that the animation generated did not significantly differ from the original video. The Sistem Usability Score (SUS) is 76.25. The score means that prototype is in the GOOD category. The average time needed to generate an animation from Indonesian input text is less than 100ms.

Awareness Modeling and Computing for Quality-Aware Coordination

In this paper, we address the issues of the trade-off between QoS and QoE with an analytical analysis based on mathematical modeling under a unified normalization measurement. We model through computation the awareness of QoS and QoE with a strategy of quality-aware QoE-QoS coordination. A balanced coordination is proposed using modeling correlations between user experience and service performance. The main contributions of this paper include three main parts. First, a comprehensive mapping is modeled in a close form to illustrate the analytic correlations between QoS, QoE, and data communication. Second, an analytical method to analyze and coordinate the nonlinear trade-off between QoE and QoS is proposed based on the theoretical proof with discussions on necessary-sufficient conditions. Third, an algorithmic framework is provided to perform QoE-QoS coordination based on quality-awareness computing with a test proof. An assessment model for user experience quantification is built with the mean opinion score (MOS) test. Quality-aware QoE and QoS models are built based on the subspace learning strategy. Simulations are given to prove the feasibility and effectiveness of the proposed method. The results show that the operations with the proposed solution can be obtained analytically with balanced efficiency in both user experience performance and network performance.

Real-time Assistive Reader Pen for Arabic Language

Disability is an impairment affecting an individual's livelihood and independence. Assistive technology enables the disabled cohort of the community to break the barriers to learning, access information, contribute to the community, and live independently. This article proposes an assistive device to enable people with visual disabilities and learning disabilities to access printed Arabic material in real-time, and to help them participate in the education system and the professional workforce. This proposed assistive device employs Optical Character Recognition (OCR) and Text To Speech (TTS) conversion, using concatenation synthesis. OCR is achieved using image processing, character extraction, and classification, while Arabic speech synthesis is achieved through concatenation synthesis, followed by Multi Band Re-synthesis Overlap-Add (MBROLA). Waveform generation in the second phase produces vocal output for the disabled user to hear. OCR character and word accuracy tests were conducted for nine Arabic fonts. The results show that six fonts were recognized with over 60% character accuracy and two fonts were recognized with over 88% accuracy. A Mean Opinion Score (MOS) test for speech quality was conducted. The results showed an overall MOS score of 3.53/5 and indicated that users were able to understand the speech. A real-time usability testing was conducted with 10 subjects. The results showed an overall average of agreements scores of 3.9/5 and indicated that the proposed Arabic reader pen meets the real-time constraints and is pleasant and satisfying to use and can contribute to make printed Arabic material accessible to visually impaired persons and people with learning disabilities.

Sistem Pakar Diagnosa Penyakit Kulit pada Manusia dengan Metode Dempster Shafer

Skin is the broadest organ in the human body that covers the entire surface of the body and acts as a support for human life. Because of its outermost location, the skin is often attacked by various diseases. This research aims to build an expert system to diagnose 10 types of skin diseases caused by viruses, fungi, bacteria, and parasites based on the knowledge of 3 experts using the Dempster Shafer method to obtain conclusions of skin diseases. Each symptom of skin disease has a value of belief that is used to calculate conclusions in the Dempster Shafer method. Expert system applications that are built can run Android-based smartphones. Testing techniques used in this research are black-box testing, theoretical calculations, system accuracy and MOS (Mean Opinion Score). Comparison of the calculation on the system has been appropriate based on the theoretical calculations. System accuracy testing in 30 sample cases resulted in an accuracy of 90%, but if it was seen as a subsection of expert diagnosis, it resulted in a system accuracy of 92.22%. MOS testing on 30 respondents results in a MOS value of 4.24 from a scale of 5 which shows that the system is proper to use and categorized into a good system.

EndoL2H: Deep Super-Resolution for Capsule Endoscopy.

Although wireless capsule endoscopy is the preferred modality for diagnosis and assessment of small bowel diseases, the poor camera resolution is a substantial limitation for both subjective and automated diagnostics. Enhanced-resolution endoscopy has shown to improve adenoma detection rate for conventional endoscopy and is likely to do the same for capsule endoscopy. In this work, we propose and quantitatively validate a novel framework to learn a mapping from low-to-high-resolution endoscopic images. We combine conditional adversarial networks with a spatial attention block to improve the resolution by up to factors of 8× , 10× , 12× , respectively. Quantitative and qualitative studies demonstrate the superiority of EndoL2H over state-of-the-art deep super-resolution methods Deep Back-Projection Networks (DBPN), Deep Residual Channel Attention Networks (RCAN) and Super Resolution Generative Adversarial Network (SRGAN). Mean Opinion Score (MOS) tests were performed by 30 gastroenterologists qualitatively assess and confirm the clinical relevance of the approach. EndoL2H is generally applicable to any endoscopic capsule system and has the potential to improve diagnosis and better harness computational approaches for polyp detection and characterization. Our code and trained models are available at https://github.com/CapsuleEndoscope/EndoL2H.

Speech quality evaluation for different pitch detection algorithms in LPC speech analysis–synthesis system

In this paper, the performance comparison of three pitch detection algorithms (PDAs) has been presented by implementing them in a LPC based speech analysis-synthesis system. The PDAs considered for comparison is based on three paradigms. The paradigms are weighted autocorrelation function (WACF), Empirical Mode Decomposition based autocorrelation function (EMD-ACF) and Empirical Mode Decomposition based average magnitude difference function (EMD-AMDF). Speech quality measurement is an important and essential task to ensure and maintain the quality of services for speech processing applications like modern telecommunication. Hence, the performance of these methods has been compared through the output speech quality using objective (perceptual evaluation of speech quality test) and subjective quality assessment (Mean Opinion Score test, diagnostic rhyme test and synthesized speech waveforms). The results show that the speech quality for the EMD-ACF and EMD-AMDF based PDA’s are better than that for WACF based PDA. The works presented in this paper is beneficial to telecommunication and speech recognition research group.

Sistem Pakar Diagnosis Penyakit Tanaman Padi Menggunakan Forward Chaining dan Dempster Shafer

Rice (Oryza sativa) is one of the main commodities in Indonesia. One of the inhibitors of rice crop production is disease. Rice plant diseases can be caused by pathogens, host plants or bad environment. The process of diagnosing rice diseases requires expertise, knowledge and experience. This study aims to build an expert system that can diagnose 13 types of rice plant diseases from 43 symptoms based on the knowledge of 3 experts with forward chaining reasoning methods and mobile-based dempster shafer calculation methods. The testing technique used is black box testing, theoretical calculation testing, system accuracy testing and MOS (Mean Opinion Score) testing. The black box test results state that the expert system has 100% compatibility in terms of functionality. The theoretical calculation test results state that the expert system calculations are in accordance with the results of manual calculations. System accuracy testing results from 30 test cases get an accuracy of 81.11%. The results of MOS testing conducted on 30 respondents produced MOS of 4.2 from a scale of 5 categorized into a good system.

Comparison of routing protocol performance on multimedia services on software defined network

Software defined network (SDN) is a new paradigm in network engineering, where control plane and data plane are separated. Data plane is carried out on each node, while a control plane is centrally located. In conventional networks, the planes are implemented in the firmware of the router. In this paper, we implemented multimedia services on SDN using exterior and interior routing protocols, such as open shortest path first (OSPF), border gateway protocol (BGP) and routing information protocol (RIP), measured the quality of service (QoS), and analyze the measurement results. The test results showed that the greater the background traffic provided, the smaller the throughput. Traffic on the network will be congested, so the available bandwidth is also increasingly used up so that the number of bits sent every second decrease. Mean opinion score (MOS) test results are 4 showing good categories based on ITU-T standards.

Rancang Bangun Aplikasi Pelaporan Keadaan Darurat di Kota Mataram(Studi Kasus Nomor Panggilan Darurat 112)

Emergencies can occur anywhere and anytime, where handling these emergencies must be done quickly. This research was conducted to design and build an emergency reporting system in the city of Mataram. The emergency reporting system that was built took the workflow from the emergency call number 112. The system will be used by 4 users namely the citizen as a emergency reporter, call taker emergency call number 112 from Diskominfo as an information processor, admin as a call taker information processor and units such as ambulances and firefighters as the action takers of the submitted report. The system runs in 2 platforms, namely the web pages used by call takers and Android, which are used by the public and action units. The system is tested using the mean opinion score and black box testing. The results obtained by using the mean opinion score test is 3.6 or the fair category.

Expert System of Diagnosing Building Damage due to Earthquake using Backpropagation Artificial Neural Network Method

Earthquake is one of the most destructive natural disasters. After the earthquake, experts were deployed to survey the damage that occurred. One of the main objectives of the assessment task carried out by experts is to evaluate and classify buildings into several categories based on the level of damage that occurs. In this study, an expert system that could facilitate the assessment of building damage due to the earthquake was made using Backpropagation neural network method. The testing techniques used in this system are blackbox, accuracy and Mean Opinion Score (MOS) testing. MOS testing conducted by 30 respondents produced an MOS value of 4.54 from a scale of 5. While the average accuracy of the system obtained is 82.22% of the 30 case cases tested by 3 building damage experts.

Empirical Mode Decomposition and Wavelet Transform Based ECG Data Compression Scheme

ObjectiveIn health-care systems, compression is an essential tool to solve the storage and transmission problems. In this regard, this paper reports a new electrocardiogram (ECG) data compression scheme which employs sifting function based empirical mode decomposition (EMD) and discrete wavelet transform. MethodEMD based on sifting function is utilized to get the first intrinsic mode function (IMF). After EMD, the first IMF and four significant sifting functions are combined together. This combination is free from many irrelevant components of the signal. Discrete wavelet transform (DWT) with mother wavelet ‘bior4.4’ is applied to this combination. The transform coefficients obtained after DWT are passed through dead-zone quantization. It discards small transform coefficients lying around zero. Further, integer conversion of coefficients and run-length encoding are utilized to achieve a compressed form of ECG data. ResultsCompression performance of the proposed scheme is evaluated using 48 ECG records of the MIT-BIH arrhythmia database. In the comparison of compression results, it is observed that the proposed method exhibits better performance than many recent ECG compressors. A mean opinion score test is also conducted to evaluate the true quality of the reconstructed ECG signals. ConclusionThe proposed scheme offers better compression performance with preserving the key features of the signal very well.

MedSRGAN: medical images super-resolution using generative adversarial networks

Super-resolution (SR) in medical imaging is an emerging application in medical imaging due to the needs of high quality images acquired with limited radiation dose, such as low dose Computer Tomography (CT), low field magnetic resonance imaging (MRI). However, because of its complexity and higher visual requirements of medical images, SR is still a challenging task in medical imaging. In this study, we developed a deep learning based method called Medical Images SR using Generative Adversarial Networks (MedSRGAN) for SR in medical imaging. A novel convolutional neural network, Residual Whole Map Attention Network (RWMAN) was developed as the generator network for our MedSRGAN in extracting the useful information through different channels, as well as paying more attention on meaningful regions. In addition, a weighted sum of content loss, adversarial loss, and adversarial feature loss were fused to form a multi-task loss function during the MedSRGAN training. 242 thoracic CT scans and 110 brain MRI scans were collected for training and evaluation of MedSRGAN. The results showed that MedSRGAN not only preserves more texture details but also generates more realistic patterns on reconstructed SR images. A mean opinion score (MOS) test on CT slices scored by five experienced radiologists demonstrates the efficiency of our methods.

Voice Conversion for Dubbing Using Linear Predictive Coding and Hidden Markov Model

Dubbing is a term used to describe filling in the sound on film or video. Voice conversion can be done to support dubbing, for purposes such as obtaining a child’s voice for dubbing on children’s films. However, problems frequently occur with this process, including difficulty finding children’s voice resources and difficulty getting children to express the desired tone and mood while recording. Therefore, in this study, we propose a method for creating a cross-gender and age voice conversion from adult voices to children’s voices. The feature extraction method that is used is Linear Predictive Coding, and the modeling method is the Hidden Markov Model. The parts synthesized are fundamental frequency (F0) and spectral content. From the simulation test, the best results for the voice conversion are achieved by Linear Predictive Coding order 19. The best state of Hidden Markov Model modeling is the 5th state. F0 Root Mean Square Error of adult men to children after the conversion increased by 57.7%, while the F0 Root Mean Square Error of adult women to children after the conversion increased by 15.29%. Root Mean Square Error Cepstral after conversion increased by 43.69%. A subjective test was also performed in terms of the mean opinion score. In terms of similarities, mean opinion score testing for Hidden Markov Model has an average value of 2.64, and in terms of quality, testing mean opinion score for Hidden Markov Model has an average value of 3.23. It is hoped that this proposed method can be used in real terms for dubbing in the film industry, especially for Indonesian dialogue.