Automatic Recording Environment Identification Using Acoustic Features

Chaudhary UA, Malik H. Automatic Recording Environment Identification Using Acoustic Features. In: AES Convention 129. Audio Engineering Society; 2010. Paper 8254. Available from: https://aes.org/publications/elibrary-page/?id=15676

@inproceedings{Chaudhary2010_15676,
  author = {Chaudhary, Usman Amin and Malik, Hafiz},
  title = {{Automatic Recording Environment Identification Using Acoustic Features}},
  booktitle = {AES Convention 129},
  note = {Paper 8254},
  year = {2010},
  month = nov,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=15676}
}

TY  - CPAPER
TI  - Automatic Recording Environment Identification Using Acoustic Features
AU  - Chaudhary, Usman Amin
AU  - Malik, Hafiz
T2  - AES Convention 129
M1  - Paper 8254
PY  - 2010
DA  - 2010/11/06
UR  - https://aes.org/publications/elibrary-page/?id=15676
PB  - Audio Engineering Society
LA  - en
AB  - Recording environment leaves its acoustic signature in the audio recording captured in it. For example, the persistence of sound, due to multiple reflections from various surfaces in a room, causes temporal and spectral smearing of the recorded sound. This distortion is referred to as audio reverberation time. The amount of reverberation depends on the geometry and composition of a recording location, the difference in the estimated acoustic signature can be used for recording environment identification. We describe a statistical framework based on maximum likelihood estimation to estimate acoustic signature from the audio recording and use it for automatic recording environment identification. To achieve these objectives, digital audio recording is analyzed first to estimate acoustic signature (in the form of reverberation time and variance of the background noise), and competitive neural network based clustering is then applied to the estimated acoustic signature for automatic recording location identification. We have also analyzed the impact of source-sensor directivity, microphone type, and learning rate of clustering algorithm on the identification accuracy of the proposed method.
ER  -