A Comparative Analysis of Classifiers and Feature Sets for Acoustic Environment Classification

Patole R, Rege P. A Comparative Analysis of Classifiers and Feature Sets for Acoustic Environment Classification. J Audio Eng Soc. 2019;67(12):939-952. doi:10.17743/jaes.2019.0042

@article{Patole2019_20705,
  author = {Patole, Rashmika and Rege, Priti},
  title = {{A Comparative Analysis of Classifiers and Feature Sets for Acoustic Environment Classification}},
  journal = {Journal of the Audio Engineering Society},
  volume = {67},
  number = {12},
  pages = {939--952},
  year = {2019},
  month = dec,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2019.0042},
  url = {https://doi.org/10.17743/jaes.2019.0042}
}

TY  - JOUR
TI  - A Comparative Analysis of Classifiers and Feature Sets for Acoustic Environment Classification
AU  - Patole, Rashmika
AU  - Rege, Priti
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 67
IS  - 12
SP  - 939
EP  - 952
PY  - 2019
DA  - 2019/12/06
DO  - 10.17743/jaes.2019.0042
UR  - https://doi.org/10.17743/jaes.2019.0042
PB  - Audio Engineering Society
LA  - en
AB  - When an audio recording is used as evidence in litigation and forensic investigations, it needs to be checked thoroughly for authenticity and integrity in order to be admissible, compelling, and decisive evidence in a court of law. An audio recording can be subject to tampering attacks with easy-to-use editing and signal processing tools, thereby undermining its legal value. Artifacts embedded in an audio recording can provide valuable clues about the acoustic environment in` which the audio was recorded and allow for the detection of tampering. This paper presents findings of two parallel methodologies: (1) where the features are extracted from the room impulse response and (2) where features are extracted directly from the reverberated recordings. These methods focus on extracting parameters from audio recordings that helped distinguish different auditory scenes. Experiments employing an exhaustive set of machine learning classifiers along with different acoustic features were conducted for the classification of auditory environments. A comparative analysis has been carried out to assess the performance of each classifier and relative performance impact of each feature set in terms of the accuracy of classification. A two-layer Artificial Neural Network (ANN) provided an accuracy of 98.7% using room impulse responses and an accuracy of 99.5% when the reverberated audio recordings were trained.
ER  -