Mudiana Mokhsin

AUTOMATIC MUSIC EMOTION CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK BASED ON VOCAL AND INSTRUMENTAL SOUND TIMBRES

Detecting emotion features in a song remains as a challenge in various area of research especially in Music Emotion Classification (MEC). In order to classify selected song with certain mood or emotion, the algorithms of the machine learning must be intelligent enough to learn the data features as to match the features accordingly to the accurate emotion. Until now, there were only few studies on MEC that exploit audio timbre features from vocal part of the song incorporated with the instrumental part of a song. Timbre features is the quality of a musical features or sound that distinguishes different types of sound production in human voices and musical instruments such as string instruments, wind instruments and percussion instruments. Most of existing works in MEC are done by looking at audio, lyrics, social tags or combination of two or more classes. The question is does exploitation of both timbre features from both vocal and instrumental sound features helped in producing positive result in MEC? Thus, this research present works on detecting emotion features in Malay popular music using artificial neural network by extracting audio timbre features from both vocal and instrumental sound clips. The findings of this research will collectively improve MEC based on the manipulation of vocal and instrumental sound timbre features, as well as contributing towards the literature of music information retrieval, affective computing and psychology.

Humanoid-Robot Intervention for Children with Autism: A Conceptual Model on FBM

Autism is a lifelong disability that affects children development in terms of social interaction, communication, and imagination. Children with autism often are not able to communicate in a meaningful way with their surroundings and could not relate to the real world. Encompassing humanoid-robot during the therapy session is said as being one of the most beneficial therapies towards these children since autistic children are reported to be keener in engaging in machinery and gadgets. Due to the limited studies in the perspective of the children’s emotions and feelings, this study adopts Kansei assessment to investigate the emotions and feelings of the autistic children while engaging with the robot. Kansei assessment was done by the teacher which interpreted the emotional responses given by the autistic children. Two autistic children were involved in the study where both of the subjects are having mild autism. The data were then analyzed and translated to Fogg’s Behavioral Model to represent the children’s learning motivation. The developed Modified Fogg’s Behavioral Model successfully shows the inter-relation between the three components of ability, trigger and motivation for the autistic children while they interact with the humanoid-robot. The final model provides some evidence that despite having limited ability, given the right intervention, the children with autism will exhibit the same level of motivation with normal children.

Automatic Music Emotion Classification Using Artificial Neural Network Based on Vocal and Instrumental Sound Timbres.

Detecting emotion features in a song remains as a c hallenge in various area of research especially in Music Emotion Classification (MEC). In order to classify selected song with certain mood or emotion, the algorithms of the machine learning must be intellig ent enough to learn the data features as to match t he features accordingly to the accurate emotion. Until now, there were only few studies on MEC that exploit audio timbre features from vocal part of the song i ncorporated with the instrumental part of a song. T imbre features is the quality of a musical features or so und that distinguishes different types of sound pr oduction in human voices and musical instruments such as st ring instruments, wind instruments and percussion instruments. Most of existing works in MEC are done by looking at audio, lyrics, social tags or combin ation of two or more classes. The question is does exploi tation of both timbre features from both vocal and instrumental sound features helped in producing pos itive result in MEC? Thus, this research present wo rks on detecting emotion features in Malay popular musi c using artificial neural network by extracting aud io timbre features from both vocal and instrumental so und clips. The findings of this research will colle ctively improve MEC based on the manipulation of vocal and instrumental sound timbre features, as well as contributing towards the literature of music inform ation retrieval, affective computing and psychology .

A review of persuasive techniques in developing children educational system

Over the last two decades, persuasive technology has become a popular research. Due to its credibility in interacting with human, computer technology is used to change human behavior and attitudes. This paper presents a review of major persuasive technologies mainly on children education. There are three (3) persuasive technologies used in developing children educational system: play-based occupational therapy or games, conversational counseling dialogue and simulated environment. A literature review of each techniques and its application in developing educational system is presented.

Humanoid robot Intervention with Autism: The Conceptual Model in Demonstrating the Emotional Responses of Children with Autism

Autism is a lifelong developmental disorder often associated with the inability to interact and communicate with others in meaningful ways. Few studies have actually proven that individuals with autism are facing difficulties in extracting and interpreting the emotion of others. This has led to the misbelief that individuals with autism are emotionless. This paper aims to demonstrate that given the right triggers, individuals with autism will show certain level of emotional responses. With its main objectives being (i) To study the previous research on the impact of Humanoid Robot Intervention on autistic children’s emotional responses and (ii) To illustrate the conceptual model that can further elevate the emotional responses of an autistic child, it is with the best hope that the model can further aid researchers in understanding the autistic children’s emotional responses while engaging with the humanoid robot, thus may help in creating a more meaningful and successful interaction session.

SMEs Maturity Model Assessment of IR4.0 Digital Transformation

This paper reports on the revolution of industry that leads to the current industry 4.0 through digital transformation. Industry 4.0 promotes innovation in organising and controlling for the entire value chain life cycle. The advancement of technology become future insights of digital transformation is aimed to have a high-flexibility, high-productivity, and become resource friendly that may provide a new level of human-machine relationship. The research is aimed in understanding the readiness of the Small Medium Enterprise (SME) in Malaysia towards digital transformation of Industry 4.0. Hence, a comparative study of the maturity assessment model towards industry 4.0 had been conducted. The IMPULS maturity assessment model is applied and six dimensions were chosen as our research model. Each dimension is explained, calculated and discussed in this paper. Each dimension is then weighted to refer to the level of readiness. The research uses questionnaires in attaining the data pertaining to the readiness of Malaysian SMEs towards industry 4.0. Next, findings of this research will be able to explain and provide a better understanding of where Malaysian SMEs stand in comparison to the maturity level that is set to test out the readiness of SMEs towards industry 4.0. This is to create and increase SMEs’ market sustainability hence creating or increasing profitability.

Change and Bug Tracking System: Anjung Penchala Sdn. Bhd.

Defects or bugs have been existed as a problem in the system and they are normally inevitable in software development. Most software bugs arise from mistakes and errors made by people in either a programs source code or its technical design. Huge amount of bugs could be found in system development. It was relatively difficult to manage bugs in simple word documents or remember everything in one’s head. Thus Change and Bug Tracking (CBT) System has been introduced in order to keep track of the reported bug in the system. By using this tracking system, every user can report and follow up the issue in the system. This system provided a platform for users from different departments to report issues and understand the latest progress in a product. It consolidated and organized information in single system and centralized data repository for the changes and bugs in system. Besides, this system could be accessed by team members concurrently to improve efficiency and they can search particular information in the system easily. It made communication between teams more effective and all the bugs and changes are recorded in web based system and hence the system is scalable. General Terms Security, system development.