Environment Sound Recognition for Digital Audio Forensics Using Linear Predictive Coding Features

AlQahtani, Mubarak Obaid; Mazyad, A. S. Al

doi:10.1007/978-3-642-22410-2_26

Cited by 3 publications

(1 citation statement)

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“…Recently, Sen et al proposed a new feature extraction technique coming from a new transformation which is based on the Nyquist filter bank and achieved significant result in speaker identification [11]. Besides, feature sets extracted from Linear Predictive coefficients (LPC) and mel-frequency cepstral coefficient (MFCC) also have powerful descriptive capability which are used frequently in gunshot detection [12], audio clips classification [13], environment sound recognition [14], and emotion recognition from speech [15].…”

Section: B Audio Featuresmentioning

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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