Enhanced Automatic Noise Removal Platform for Broadcast, Forensic, and Mobile Applications

Saeed S, E.V. H, Sinha D, Vodapally B. Enhanced Automatic Noise Removal Platform for Broadcast, Forensic, and Mobile Applications. In: AES Convention 127. Audio Engineering Society; 2009. Paper 7869. Available from: https://aes.org/publications/elibrary-page/?id=15064

@inproceedings{Saeed2009_15064,
  author = {Saeed, Shamail and E.V., Harinarayanan and Sinha, Deepen and Vodapally, Balaji},
  title = {{Enhanced Automatic Noise Removal Platform for Broadcast, Forensic, and Mobile Applications}},
  booktitle = {AES Convention 127},
  note = {Paper 7869},
  year = {2009},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=15064}
}

TY  - CPAPER
TI  - Enhanced Automatic Noise Removal Platform for Broadcast, Forensic, and Mobile Applications
AU  - Saeed, Shamail
AU  - E.V., Harinarayanan
AU  - Sinha, Deepen
AU  - Vodapally, Balaji
T2  - AES Convention 127
M1  - Paper 7869
PY  - 2009
DA  - 2009/10/06
UR  - https://aes.org/publications/elibrary-page/?id=15064
PB  - Audio Engineering Society
LA  - en
AB  - We present new enhancements and additions to our novel Adaptive/Automatic Wide Band Noise Removal (AWNR) algorithm proposed earlier. AWNR uses a novel framework employing dominant component subtraction followed by adaptive Kalman filtering and subsequent restoration of the dominant components. The model parameters for Kalman filtering are estimated utilizing a multi-component Signal Activity Detector (SAD) algorithm. The enhancements we present here include two enhancements to the core filtering algorithm, including the use of a multi-band filtering framework as well as a color noise model. In the first case it is shown how the openness of the filtered signal improves through the use of a two band structure with independent filtering. The use of color noise model on the other hand improves the level of filtering for wider types of noises. We also describe two other structural enhancements to the AWNR algorithm which allow it to better handle respectively dual microphone recording scenarios and forensic/restoration applications. Using an independent capture from a noise microphone the level of filtering is substantially increased. Furthermore for forensic applications a two/multiple pass filtering framework in which SAD profiles may be fine tuned using manual intervention are desirable.
ER  -