Recording environment identification using acoustic reverberation

Acoustic environment leaves its fingerprints in the audio recording captured in it. Acoustic reverberation and background noise are generally used to characterize an acoustic environment. Acoustic reverberation depends on the shape and the composition of a room, therefore, differences in the estimated reverberation can be used in a forensic and ballistic settings and acoustic environment identification (AEI). We describe a framework that uses acoustic reverberation to characterize recording environment and use it for AEI. Inverse filtering is used to estimate the reverberation component from audio recording. A 48-dimensional feature vector consisting of Mel-frequency Cepstral Coefficients and Logarithmic Mel-spectral Coefficients is used to capture traces of reverberation. A multi-class support vector machine (SVM) classifier is used for AEI. Experimental results show that the proposed system can successfully identify a recording environment for regular as well as blind AEI.