Performance of Closed-Form Acoustic Scene Decomposition for Forensic Analysis

Günel B, Nikolopoulos G, Hacihabiboglu H, Kondoz AM. Performance of Closed-Form Acoustic Scene Decomposition for Forensic Analysis. In: AES Conference: 39th International Conference: Audio Forensics: Practices and Challenges. Audio Engineering Society; 2010. Paper 4-1. Available from: https://aes.org/publications/elibrary-page/?id=15489

@inproceedings{Gunel2010_15489,
  author = {Günel, Banu and Nikolopoulos, Grigorios and Hacihabiboglu, Hüseyin and Kondoz, Ahmet M.},
  title = {{Performance of Closed-Form Acoustic Scene Decomposition for Forensic Analysis}},
  booktitle = {AES Conference: 39th International Conference: Audio Forensics: Practices and Challenges},
  note = {Paper 4-1},
  year = {2010},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=15489}
}

TY  - CPAPER
TI  - Performance of Closed-Form Acoustic Scene Decomposition for Forensic Analysis
AU  - Günel, Banu
AU  - Nikolopoulos, Grigorios
AU  - Hacihabiboglu, Hüseyin
AU  - Kondoz, Ahmet M.
T2  - AES Conference: 39th International Conference: Audio Forensics: Practices and Challenges
M1  - Paper 4-1
PY  - 2010
DA  - 2010/06/06
UR  - https://aes.org/publications/elibrary-page/?id=15489
PB  - Audio Engineering Society
LA  - en
AB  - In-situ audio recordings for forensic analysis are generally made using purpose-installed microphones in order to capture speech and other relevant sounds in an environment. Often, post-processing efforts are focused on cleaning these recordings from interference. An acoustic scene is made up of audio objects and their not only contents, but also positions carry evidential importance. Therefore, in addition to cleaning target sources, detecting their locations may be required. This paper focuses on the performance of a closed-form acoustic scene decomposition technique, which blindly decomposes the sound field using signals obtained from a coincident microphone array. This decomposition both localises the audio objects and enhances them, thereby improving the intelligibility of speech signals. Detailed testing has been carried out in order to quantify the localisation, separation, speech intelligibility improvement and automatic speech recognition performances of the system. Multiple speech sources in both isotropic and directional fluctuating noise under typical application scenarios have been considered.
ER  -