Using One-Class SVMs and MP for Audio Recognition of Action Scenes

Guo, Fei; Shan, Shuqian; Wang, Xiaohui

doi:10.1109/etcs.2010.164

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…As a result, the updating process of microphone authentication system is simpler and cheaper than supervised learning approach. Despite the fact that the OCC approach has been successfully employed in several audio forensic tasks such as sound classification [6], [7], scene classification [8], and speaker verification [9], so far no work has been found in the field of microphone forensics where its application scenario is considered appealing.…”

Section: Problem Definition and Research Objectivementioning

confidence: 99%

Identifying Microphone from Noisy Recordings by Using Representative Instance One Class-Classification Approach

Vu¹,

Liu²,

Yang³

et al. 2012

JNW

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Rapid growth of technical developments has created huge challenges for microphone forensics - a sub-category of audio forensic science, because of the availability of numerous digital recording devices and massive amount of recording data. Demand for fast and efficient methods to assure integrity and authenticity of information is becoming more and more important in criminal investigation nowadays. Machine learning has emerged as an important technique to support audio analysis processes of microphone forensic practitioners. However, its application to real life situations using supervised learning is still facing great challenges due to expensiveness in collecting data and updating system. In this paper, we introduce a new machine learning approach which is called One-class Classification (OCC) to be applied to microphone forensics; we demonstrate its capability on a corpus of audio samples collected from several microphones. In addition, we propose a representative instance classification framework (RICF) that can effectively improve performance of OCC algorithms for recording signal with noise. Experiment results and analysis indicate that OCC has the potential to benefit microphone forensic practitioners in developing new tools and techniques for effective and efficient analysis

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