DocumentCode :
87080
Title :
Audio Recording Location Identification Using Acoustic Environment Signature
Author :
Hong Zhao ; Malik, Haroon
Author_Institution :
Sch. of Sci. & Technol., Southwest Jiaotong Univ., Chengdu, China
Volume :
8
Issue :
11
fYear :
2013
fDate :
Nov. 2013
Firstpage :
1746
Lastpage :
1759
Abstract :
An audio recording is subject to a number of possible distortions and artifacts. Consider, for example, artifacts due to acoustic reverberation and background noise. The acoustic reverberation depends on the shape and the composition of the room, and it causes temporal and spectral smearing of the recorded sound. The background noise, on the other hand, depends on the secondary audio source activities present in the evidentiary recording. Extraction of acoustic cues from an audio recording is an important but challenging task. Temporal changes in the estimated reverberation and background noise can be used for dynamic acoustic environment identification (AEI), audio forensics, and ballistic settings. We describe a statistical technique based on spectral subtraction to estimate the amount of reverberation and nonlinear filtering based on particle filtering to estimate the background noise. The effectiveness of the proposed method is tested using a data set consisting of speech recordings of two human speakers (one male and one female) made in eight acoustic environments using four commercial grade microphones. Performance of the proposed method is evaluated for various experimental settings such as microphone independent, semi- and full-blind AEI, and robustness to MP3 compression. Performance of the proposed framework is also evaluated using Temporal Derivative-based Spectrum and Mel-Cepstrum (TDSM)-based features. Experimental results show that the proposed method improves AEI performance compared with the direct method (i.e., feature vector is extracted from the audio recording directly). In addition, experimental results also show that the proposed scheme is robust to MP3 compression attack.
Keywords :
audio coding; audio recording; audio watermarking; ballistics; digital forensics; feature extraction; interference suppression; nonlinear filters; particle filtering (numerical methods); reverberation; spectral analysis; vectors; MP3 compression attack; TDSM-based features; acoustic cues; acoustic environment signature; acoustic environments; acoustic reverberation; audio forensics; audio recording location identification; background noise estimation; ballistic settings; commercial grade microphones; direct method; dynamic acoustic environment identification; evidentiary recording; feature vector; full-blind AEI; microphone independent AEI; nonlinear filtering; particle filtering; reverberation noise; secondary audio source activity; semiblind AEI; spectral smearing; spectral subtraction; speech recordings; statistical technique; temporal derivative-based spectrum and mel-cepstrum-based features; temporal smearing; Audio recording; Background noise; Microphones; Noise measurement; Particle filters; Reverberation; Acoustic environment identification; acoustic reverberation; audio forensics; background noise; particle filtering;
fLanguage :
English
Journal_Title :
Information Forensics and Security, IEEE Transactions on
Publisher :
ieee
ISSN :
1556-6013
Type :
jour
DOI :
10.1109/TIFS.2013.2278843
Filename :
6582548
Link To Document :
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