• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.3
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• pp.485-492
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• 2017

GMM has been one of the most popular methods for scream sound detection. In the conventional GMM, the whole training data is divided into scream sound and non-scream sound, and the GMM is trained for each of them in the training process. Motivated by the idea that the process of scream sound detection is very similar to that of speaker recognition, the UBM which has been used quite successfully in speaker recognition, is proposed for use in scream sound detection in this study. We could find that UBM shows better performance than the traditional GMM from the experimental results.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.1133-1136
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• 2017

폐쇄회로 텔레비전(CCTV, Closed Circuit Television)은 범죄 예방을 위해서 현재 많이 사용되고 있다. 하지만, CCTV의 경우 주로 영상에 기반하기 때문에 사각지대가 존재할 수 있으며 범죄 발생시 즉각적인 대처보다는 후속 조치에 주로 활용된다. 본 논문에서는 이와 같은 문제점을 해결하기 위해 비명을 탐지할 수 있는 치안 향상을 위한 비명 감지 시스템을 설계하고 구현하였다. 제안하는 시스템은 비명을 감지하면 경보음을 제공해주며, 중앙 시스템에 알림을 주어 관리자가 확인하고 즉각적인 대응이 가능할 수 있도록 할 수 있는 기능을 제공한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.183-190
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• 2011

In this paper, we propose a prototype system for abnormal sound detection and identification which detects and recognizes the abnormal situations by means of analyzing audio information coming in real time from CCTV cameras under surveillance environment. The proposed system is composed of two layers: The first layer is an one-class support vector machine, i.e., support vector data description (SVDD) that performs rapid detection of abnormal situations and alerts to the manager. The second layer classifies the detected abnormal sound into predefined class such as 'gun', 'scream', 'siren', 'crash', 'bomb' via a sparse representation classifier (SRC) to cope with emergency situations. The proposed system is designed in a hierarchical manner via a mixture of SVDD and SRC, which has desired characteristics as follows: 1) By fast detecting abnormal sound using SVDD trained with only normal sound, it does not perform the unnecessary classification for normal sound. 2) It ensures a reliable system performance via a SRC that has been successfully applied in the field of face recognition. 3) With the intrinsic incremental learning capability of SRC, it can actively adapt itself to the change of a sound database. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.