Ming-Yen Chen

A semantic-based approach to content abstraction and annotation for content management

In recent years, knowledge becomes the most important asset of individuals as well organizations, and determines the competitiveness of an enterprise. Content is a knowledge container that implies what human beings transform their knowledge in when they want to communicate with other people. Therefore, effective content management can achieve the goal and value of knowledge management. Content management activities are depending on meta-content which represents the content feature and are generated through the procedures of content abstraction and annotation. In general, the content creator utilizes words and logical combinations to express his intentions and concepts in semantic formats that are comprehensible to human beings. Accordingly, managing content from a semantic approach can effectively improve transparency and visibility of the content, and guides both the content creator and the user to engage in seamless, semantic-based communications and interactions. This study proposes a semantic-based content abstraction and annotation approach and a semantic pattern for bridging the semantic gaps of content management. Based on this approach, a semantic-driven content management environment that features a high interoperability of content can be constructed to accomplish the following goals and benefits: (1) effectively bridging semantic gaps in the existing content management systems; (2) delivering the right content to the right user at the right time; (3) bridging the semantic gaps for the content author and the customer to increase the efficiency of content management; and (4) realizing the acquisition, analysis, access, management, diffusion, interpretation, and innovation of knowledge.

Development of a semantic-based content mapping mechanism for information retrieval

Most information retrieval systems use keywords entered by the user as the search criteria to find documents. However, the language used in documents is often complicated and ambiguous, and thus the results obtained by using keywords are often inaccurate. To address this problem, this study developed a semantic-based content mapping mechanism for an information retrieval system. This approach employs the semantic features and ontological structure of the content as the basis for constructing a content map, thus simplifying the search process and improving the accuracy of the returned results.

Automatic singing evaluating system based on acoustic features and rhythm

This paper proposes an automatic singing evaluation system. The system provides the user with the score that was assessed by acoustic features and the rhythmic similarity between the original song and the user input. The assessment system is divided into two stages. In the first stage, acoustic similarities are measured by dynamic time warping (DTW). In the second stage, the rhythmic similarity is measured by analysing the optimal path of DTW by quadratic polynomial regression. Finally, the similarity of two stages is combined into one score by corresponding weights. The experimental results show the good performance for the automatic singing evaluation system.

Acoustic scene change detection

This work proposes an acoustic scene change detection approach in the indoor space by reverberation. Speakers emit impulse signals periodicity. Sound waves are then influenced with objects, the floor, walls, and roofs, and generate corresponding reverberation. This reverberation can represent the characteristic of the indoor space, which is called acoustic scene. When the state of the indoor space changes, such as furniture changes positions, windows are open or closed, and the number of people in the room increases or decreases, the acoustic scene would differ from the one before. In this way, we can evaluate whether the indoor scene changes or not. This work presents the difference between the current acoustic scene and the initial acoustic scene by calculating the difference between the current spectrogram and the initial spectrogram of the reverberation. When the sum of acoustic scene differences is greater than the threshold decided in the training stage, we say that acoustic scene change occurs.

Development of a Semantic Awareness Framework for Textual Content Management in e-Learning

This study proposes a semantic-awareness content management model, based on which a semantic-based environment that features a high interoperability of e-Learning content can be constructed, to realize the goals and values of e-Learning by effectively bridging semantic gaps in existing LCMSs.

Acoustic scene change detection by spectro-temporal filtering on spectrogram using chirps

Indoor acoustic scene change detection systems using periodic impulse signals were developed in the past. Comparing with impulse signals, the chirp signal is more robust against environmental noise due to its specific spectro-temporal structure, which can be easily detected using a spectro-temporal modulation filtering mechanism. In this paper, we demonstrate a system which employs the two-dimensional spectro-temporal filtering mechanism on Fourier spectrogram to measure the total energy of reverberations of the chirp signal and compares the energy difference between consecutive chirps with a predefined threshold to automatically detect the change of the acoustic scene. Simulation results show the proposed system is very effective in detecting the acoustic scene change in a real living room with great robustness against noise recorded from real-world.

System implementation of robust time difference of arrival estimation

We combine sound activity detection, sound enhancement, and direction of arrival (DOA) estimation system to an integrated system. We use sound activity detection for finding frames which are sound-dominated, then pass the signal to the subspace based sound enhancement for denoising. Finally, the denoised signal will be input to DOA detection system for sound tracking. The generalized cross-correlation and phase transformation (GCC-PHAT) based time difference of arrival (TDOA) estimation can detect the TDOA of the sound signal and then calculate the respondent DOA.