Reviewerly/exHarmony huggingface dataset:
Harmonic-plus-Noise Network with Linear Prediction and Perceptual Weighting Filters for Filipino Speech Synthesis

Vocoders in Text-To-Speech (TTS) systems are responsible for converting acoustic feature representations such as the Mel Spectrogram to the sound waveform. Recent developments in vocoders, such as WaveRNN [1], Parallel WaveGAN [2], HiFi-GAN [3], and Diffusion models [4], [5], mostly have introduced neural architectures outperforming traditional architectures like those using the Griffin-Lim algorithm (GLA)[6]. In this paper, a multi-band Parallel WaveGAN architecture (PWG), the Harmonic-plus-Noise (H+N) vocoder, is trained, implemented, and combined with two types of filters: a) Linear Prediction (LP) filter and b) Perceptual Weighting (PW) filter to improve the TTS quality in Filipino language. Based on the results, HN-PWG garnered the highest total MOS at 4.59 ± 0.10, closely followed by HN-PWG-PW at 4.58 ± 0.07 with no statistically significant difference between the two. All the implemented H+N systems were able to outperform the Tacotron2-based Filipino TTS using a WaveGlow vocoder based on the MOS.
Comparison of Data Augmentation Techniques on Filipino ASR for Children’s Speech

While recent advances in automatic speech recognition (ASR) systems involve neural architectures and large acoustic models with latent feature representations, problem arises for low-resource languages- especially when dealing with children’s speech that has very limited data. In this paper, recent data augmentation techniques such as spectral warping (SW), vocal tract length perturbation (VTLP), spectrogram augmentation (SpecAug), and MaskCycleGAN-VC were used on the Tagalog ISIP06 corpus. Moreover, experiments were designed to determine the optimal parameters for each technique and to evaluate the systems using different combinations of DA in terms of the word error rate (WER) and relative improvement (RI) upon testing with actual children’s speech data. Based on the results, the combined augmented data which yielded the best-performing system recorded the lowest WER of 11.72% corresponding to a 43.55% relative improvement with respect to the baseline system.
Data Center Audio/Video Intelligence on Device (DAVID) - An Edge-AI Platform for Smart-Toys

An overview is given of the DAVID Smart-Toy platform, one of the first Edge-AI platform designs to incorporate advanced low-power data processing by neural inference models co-located with the relevant image or audio sensors. There is also on-board capability for in-device text-to-speech generation. Two alternative embodiments are presented – a smart Teddy-bear and a roving dog-like robot. The platform offers a speech-driven user interface and can observe and interpret user actions and facial expressions via its computer vision sensor node. A particular benefit of this design is that no personally identifiable information passes beyond the neural inference nodes thus providing inbuild compliance with data protection regulations.
Fast 3-D Measurement Based on Spatial-temporal Speckle Projection

Speckle projection profilometry (SPP), as a promising structured light projection technique, can achieve global unambiguous 3D measurement by projecting a single random speckle pattern. However, due to the measured surfaces with complex reflection characteristics and the perspective differences between stereo cameras, only projecting a single speckle pattern is difficult to encode a globally unique feature for every pixel in the whole measurement space, resulting in its limited measurement accuracy. To meet the high-precision measurement requirement of industrial scenes, this paper proposed a 3D measurement method based on spatial-temporal speckle projection (STSP), which obtain the depth information of the measured scene efficiently by integrating 10 speckle projection modules. Experimental results demonstrate that, within the measurement range of 0.8m-2.0m, the proposed method can achieve fast and high-precision 3D measurement with the accuracy of 0.43mm at 0.85m.
Enhancing End-to-End Speech Synthesis by Modeling Interrogative Sentences with Speaker Adaptation

Despite end-to-end text-to-speech (TTS) synthesizers producing human-like speech, they might still need more intuitive user control over prosody. Modeling interrogative sentence prosody is challenging due to the significant variation in question types. Synthesized intonation frequently requires more accuracy, richness, and detail when only a small amount of adaptation data from particular sentence types are available. This paper uses speaker adaptation to enhance the modeling of interrogative sentence prosody in speech synthesis, tested on an English dataset. The adaptation data were selected based on the occurrence of interrogative sentences. The first dataset consisted of sentences with frequent interrogative sentences, whereas the second dataset contained declarative sentences. Two target speakers (male and female) were adapted. Objective and subjective evaluations show that the proposed model achieves remarkable performance in intonation. The MUSHRA subjective listening test has shown better intonation patterns using the interrogative dataset than the declarative one. The potential application of this model is for the vision impaired and chatbots/voice bots.
The Course Design of Basic Data Science Taking into Account Both Face-To-Face and On-Demand Teaching and Effect Analysis

We have conducted research on the construction of educational curricula and proposed a stage-wised refinement curriculum model for data science education at liberal arts universities. The model consists of four stages: Data Science Invitation, Data Science Basics, Data Science Practice, and Data Science Development. Based on this model, we have developed educational subjects related to the three knowledge areas, implemented them in our university’s data science education program, and are conducting educational practice. In this paper, we focus on the subject of "Data Science" in Area II that we oversaw in the second half of 2022, and report on syllabus design, content creation, implementation status, and evaluation of its on-demand implementation. The researchers conducted both face-to-face and on-demand classes for proven that the content of the course is effective.
Growing Elliptical RBF Network for Power Plant Return Water Temperature Model

The study aims to improve the radial basis function (RBF) neural network learning method based on a genetic algorithm to speed up finding optimal parameters of a combined heat and power (CHP) plant return water temperature model. A growing hidden layer approach is used to simplify network learning and avoid overfitting. The test results prove that the proposed initialization of an elliptical RBF network with only a small number of active neurons allows reaching the desired accuracy faster for both the standard deviation and the maximum absolute prediction error. Learning is performed on the Kharkiv CHP plant #5 heat output data.
Construction of a calendar conversion system with a function to interpret Chinese calendar date expressions

The Chinese calendar had been used in historical documents across East Asian countries. In order to organize and visualize historical records described in documents based on the Chinese calendar, in chronological order, and to compare these with records based on other calendars, a function is required to interpret the Chinese calendar date expressions and to convert these into positions on a common temporal axis. This study proposed a function to interpret date expressions of the Chinese calendar, such as "宣統三年十二月廿五", represented by using Chinese characters, and to convert these into the Julian dates. This function directly interprets the complex Chinese calendar date expressions, and is distinct from conventional calendar conversion systems, which require entering the era name and numbers of year, month, and day into separate text boxes. This function can be easily used from external applications and databases, through the Web Application Programing Interface (API). The function is expected to contribute to the advancement of historical studies in both China and East Asia as a whole.
Finding Tipping Points in Web Text Data

With the spread of the Internet, large volumes of text data related to local communities or societies are being distributed on the Web, and the texts’ contents change significantly with large-scale disasters, political changes, or national elections, among others. If we can estimate the moments at which the texts’ contents change, we can consider that it indicates the possibility that some change occurred in society. Based on the above, this paper explores a new direction for area studies based on informatics compatible with the Internet age to detect Tipping Points in areas.
Heritage Museum Evolution: Virtual Realitizing Heritage Museums in Taiwan with An Exploratory Virtual Reality Museum Project

Aboriginal heritage museums in Taiwan have long faced a dilemma: they rely on tourism for financial support while also aiming to preserve their culture and heritage, safeguarding them from external cultural influences. The purpose of this research paper is to propose a solution that harnesses state-of-the-art Virtual Reality (VR) technology to virtual realitizing Taiwan's heritage museums. This approach aims to address, or significantly mitigate, the conflict, while simultaneously offering an immersive digital archiving experience and propagating the heritage and material culture through the virtual presence of VR museum users. Additionally, an exploratory VR heritage museum has been developed to serve as both an actual experiment and a preliminary assessment of the VR application's development.
Modeling Irregular Voice in End-to-End Speech Synthesis via Speaker Adaptation

End-to-end text-to-speech (TTS) synthesizers may not create a speech similar to the target speaker when the adaptation data is limited or/and chosen randomly. Creaky voice might occur frequently, depending on the speaker and the context. This paper uses speaker adaptation to model creaky voice in speech synthesis. We adapted FastSpeech 2 with four target speakers by selecting the adaptation data based on the occurrence of creaky phonation: 1) sentences with frequent creaky voice, 2) randomly chosen sentences, and 3) sentences with few creaky voice. In an objective evaluation, the proposed model successfully modeled creaky voice using data selection (1), producing speech with more creakiness than the other data selections. A subjective test revealed that these frequent creaky voice synthesized samples (for the average of four speakers) obtained slightly less preference than the synthesized speech from a few creaky voice adaptation sentences. Irregular voice models might contribute to building emotional and personalized speech synthesis.
Autonomous Landing of an Unmanned Aerial Vehicle on a Mobile Platform

Landing an Unmanned Aerial Vehicle (UAV) on a mobile vehicle, has been an active research area due to its much needed applicability in numerous directions such as landing an aerial vehicle on the international space station or a ship at sea. Such research is conducted mainly in two directions: control-based and vision-based. Vision-based approaches require sensing from cameras that detect features, such as visual markers, on the mobile vehicle to estimate and track the mobile vehicle pose till the landing pose. The visual markers are usually utilized for feature detection, which most mobile vehicles might not have. This paper proposes a new approach for achieving autonomous landing of a UAV by generating constrained uniform B-spline trajectories assuming known pose of both the UAV and the UGV. Then applying PID controller to regulate velocity set point to land the UAV on the mobile vehicle. The overall effectiveness of the proposed approach is demonstrated using ROS/Gazebo simulations, with a UAV (Px4 enabled IRIS) and a mobile vehicle (Jackal from ClearPath Robotics). Landing tasks for static and dynamic cases are carried out and the findings and limitations are stated thereafter.
Ant Colony Optimization-Based Path Planning for UAV Navigation in Dynamic Environments

In a dynamic environment, path planning for Unmanned Aerial Vehicles (UAVs) is challenging due to obstacles and changing conditions. To solve this, bio-inspired metaheuristic algorithms such as Ant Colony Optimization (ACO) have shown promising result in solving optimization problems. In this paper we have presented ACO-based path planning algorithms for UAV navigation in dynamic environments. We determined the optimal paths for UAVs based on swarm intelligence and ant behavior. Pheromone trails and attractive heuristics help the UAVs navigate safely and efficiently, adapting to their environment dynamically. The results of our simulation analysis show that the proposed algorithm is both effective and efficient for a dynamic environment. With its ability to handle a variety of dynamic scenarios, ACO is ideal for UAV applications like surveillance, delivery, and search and rescue missions. In the simulation environment we have simulated 5 different scenarios on the grid and the results show that the algorithm is able find the optimum path efficiently in the dynamic environment with the obstacles. We plan to improve accuracy with a hybrid approach in the future.
The Two Stage Method for Detecting Objects of Interest on Optical Satellite RGB Imagery of the Urban Infrastructure

It is proposed to develop the two stages method for detecting objects of interest on optical satellite RGB imagery of the urban infrastructure. The paper analyzes approaches to the detection of urban infrastructure objects on remote sensing imagery of the earth. It presents their advantages and disadvantages. It is proposed to use one of the methods of swarm intelligence, namely, the ant colony optimization algorithm at the first stage. At the second stage, the Hough transform is used to extract straight lines from the total number of detectted edges at the first stage. It is presented the results of segmentation of optical satellite RGB image of the urban infrastructure by the proposed method after each of the stages. The evaluation of the quality of the results of the method was only visual.
Exploring Maritime History through Digital Humanities: The Southeast Asian Maritime Travelogues Corpus (SEA-MTC)

Maritime history encompasses a diverse range of primary data sources essential for digital humanities research. Although there are several digital libraries and repositories for maritime historical materials, the availability of open-access digital humanities platforms focused on travelogues remains limited. Southeast Asian Maritime Travelogues Corpus (SEA-MTC) platform has been proposed to create an open digital humanities platform specifically focused on Southeast Asian maritime travelogues. It will incorporate a full-text search function and various other digital humanities tools. This paper outlines the current plan and future developments for the SEA-MTC platform, which includes the utilization of Optical Character Recognition (OCR) and Named Entity Recognition (NER) processes for text and entity extraction, along with other relevant tools and functionalities.
Automated Child Voice Generation: Methodology and Implementation

Significant progress has been made in the development of text-to-speech (TTS) models; however, synthesizing child speech remains a challenging task. Limited research has been conducted on this topic due to the lack of child speech datasets and the inherent difficulties in constructing such datasets. Children's speech is often less clear and exhibits significant variations in terms of volume, pitch, and rhythm. In this study, we explore the use of two different vocoders for synthesizing conversational multi-speaker child speech: the WORLD vocoder based on statistical parametric speech synthesis (SPSS) and the neural vocoders based on Parallel WaveGAN and AutoVocoder. Initially, we trained the AutoVocoder on a dataset of female adult speech. Subsequently, we investigated the effectiveness of fine-tuning and adapting these vocoders to capture the unique characteristics of child speech, while mitigating the need for extensive child speech datasets. Experimental results demonstrated that the AutoVocoder outperformed other vocoders in terms of clarity when synthesizing conversational multi-speaker child speech. Despite the challenges posed by the MyST child datasets used in this study, which included non-phonetic noise and indiscernible speech, the AutoVocoder significantly improved the quality and clarity of the ground truth in the context of conversational multi-speaker child speech synthesis. Both objective and subjective evaluations indicated that the original and synthesized speech by the AutoVocoder were very similar to each other.
Improved Child Text-to-Speech Synthesis through Fastpitch-based Transfer Learning

Speech synthesis technology has witnessed significant advancements in recent years, enabling the creation of natural and expressive synthetic speech. One area of particular interest is the generation of synthetic child speech, which presents unique challenges due to children's distinct vocal characteristics and developmental stages. This paper presents a novel approach that leverages the Fastpitch text-to-speech (TTS) model for generating high-quality synthetic child speech. This study uses the transfer learning training pipeline. The approach involved finetuning a multi-speaker TTS model to work with child speech. We use the ‘cleaned’ version of the publicly available MyST dataset (55 hours) for our finetuning experiments. We also release a prototype dataset of synthetic speech samples generated from this research together with model code to support further research. By using a pretrained MOSNet, we conducted an objective assessment that showed a significant correlation between real and synthetic child voices. Additionally, to validate the intelligibility of the generated speech, we employed an automatic speech recognition (ASR) model to compare the word error rates (WER) of real and synthetic child voices. The speaker similarity between the real and generated speech is also measured using a pretrained speaker encoder.
Neighborhood Learning for Artificial Bee Colony Algorithm: A Mini-survey

Artificial bee colony (ABC) algorithm is a representative paradigm of swarm intelligence optimization (SIO) algorithms, which has received much attention in the field of global optimization for its good performance yet simple structure. However, there still exists a drawback for ABC that it owns strong exploration but weak exploitation, resulting in slow convergence speed and low convergence accuracy. To solve this drawback, in recent years, the neighborhood learning mechanism has emerged as an effective method, becoming a hot research topic in the community of ABC. However, there has been no surveys on it, even a short one. Considering the appeal of the neighborhood learning mechanism, we are motivated to provide a mini-survey to highlight some key aspects about it, including 1) how to construct a neighborhood topology? 2) how to select the learning exemplar? and 3) what are the advantages and disadvantages? In this mini-survey, some related neighborhood-based ABC variants are reviewed to reveal the key aspects. Furthermore, some interesting future research directions are also given to encourage deeper related works.
Multi-task Learning Network for Automatic Pancreatic Tumor Segmentation and Classification with Inter-Network Channel Feature Fusion

Pancreatic cancer is a malignant tumor with a high mortality rate. Therefore, accurately identifying pancreatic cancer is of great significance for early diagnosis and treatment. Currently, several methods have been developed using network structures based on multi-task learning to address tumor recognition issues. One common approach is to use the encoding part of a segmentation network as shared features for both segmentation and classification tasks. However, due to the focus on detailed features in segmentation tasks and the requirement for more global features in classification tasks, the shared features may not provide more discriminatory feature representation for the classification task. To address above challenges, we propose a novel multi-task learning network that leverages the correlation between the segmentation and classification networks to enhance the performance of both tasks. Specifically, the classification task takes the tumor region images extracted from the segmentation network's output as input, effectively capturing the shape and internal texture features of the tumor. Additionally, a feature fusion module is added between the networks to facilitate information exchange and fusion. We evaluated our model on 82 clinical CT image samples. Experimental results demonstrate that our proposed multi-task network achieves excellent performance with a Dice similarity coefficient (DSC) of 88.42% and a classification accuracy of 85.71%.
Interactive Selection Recommendation Based on the Multi-head Attention Graph Neural Network

The click-through rate prediction of users is a critical task in the recommendation system. As a powerful machine learning method, graph neural networks have been favored by scholars to solve the task recently. However, most graph neural network-based click-through rate prediction models ignore the effectiveness of feature interaction and generally model all feature combinations, even if some are meaningless. Therefore, this paper proposes a Multi-head attention Graph Neural Network with Interactive Selection, named MGNN_IS in short, to capture the complex feature interactions via graph structures. In particular, there are three sub-graphs to be constructed to capture internal information of users and items respectively, and interactive information between users and items, namely the user internal graph, item internal graph, and user-item interaction graph correspondingly. Moreover, the proposed model designs a multi-head attention propagation module for the aggregation with an interactive selection strategy. This module can select the constructed graph and increase diversity with multiple heads to achieve the high-order interaction from the multiple layers. Finally, the proposed model fuses the features, and predicts. Experiments on three public datasets demonstrate that the proposed model outperformed other advanced models.
Contextual Qualitative Deterministic Models for Self-learning Embodied Agents

This work presents an approach for embodied agents that have to learn models from the least amount of prior knowledge, solely based on knowing which actions can be performed and observing the state. Instead of relying on (often black-box) quantitative models, a qualitative forward model is learned that finds the relations among the variables, the contextual relations, and the qualitative influence. We assume qualitative determinism and monotonicity, assumptions motivated by human learning. A learning and exploitation algorithm is designed and demonstrated on a robot with a gripper. The robot can grab an object and move it to another location, without predefined knowledge of how to move, grab or displace objects.