• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.207-220
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• 2021

Recent advances in new media for sensory experiences keep expanding visual expression methods in installation art such as projection mapping and virtual reality. Artists can create and develop visual expression techniques based on such new media. Projection mapping is a new medium that continues to add various possibilities to visual expression in media art. Under the projection mapping environment, artists can recompose the object or space with the digital content by projecting video onto three-dimensional surfaces in the space. This paper focuses on the process where visual expression with the projection mapping technology leads to viewers' sensory experience. To this end, "reproducibility," "dissemination," "virtuality," and "interactivity" of media were analyzed to describe the meaning and *definition of visual expression. Artworks are considered as an example to study visual expression techniques such as "repetition and overlap," "simulacrum and metaphor," and "displacement and conversion." I applied the analysis and created Domino, a projection mapping artwork, which helps the research on visual expression techniques that can lead to sensory experience the extensibility of visual expression.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.536-543
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• 2023

Speaker diarization, which labels for "who spoken when?" in speech with multiple speakers, has been studied on a deep neural network-based end-to-end method for labeling on speech overlap and optimization of speaker diarization models. Most deep neural network-based end-to-end speaker diarization systems perform multi-label classification problem that predicts the labels of all speakers spoken in each frame of speech. However, the performance of the multi-label-based model varies greatly depending on what the threshold is set to. In this paper, it is studied a speaker diarization system using single-label classification so that speaker diarization can be performed without thresholds. The proposed model estimate labels from the output of the model by converting speaker labels into a single label. To consider speaker label permutations in the training, the proposed model is used a combination of Permutation Invariant Training (PIT) loss and cross-entropy loss. In addition, how to add the residual connection structures to model is studied for effective learning of speaker diarization models with deep structures. The experiment used the Librispech database to generate and use simulated noise data for two speakers. When compared with the proposed method and baseline model using the Diarization Error Rate (DER) performance the proposed method can be labeling without threshold, and it has improved performance by about 20.7 %.