Loudspeaker and Room Response Modeling with Psychoacoustic Warping, Linear Prediction, and Parametric Filters

Bharitkar S, Kyriakakis C. Loudspeaker and Room Response Modeling with Psychoacoustic Warping, Linear Prediction, and Parametric Filters. In: AES Convention 121. Audio Engineering Society; 2006. Paper 6982. Available from: https://aes.org/publications/elibrary-page/?id=13816

@inproceedings{Bharitkar2006_13816,
  author = {Bharitkar, Sunil and Kyriakakis, Chris},
  title = {{Loudspeaker and Room Response Modeling with Psychoacoustic Warping, Linear Prediction, and Parametric Filters}},
  booktitle = {AES Convention 121},
  note = {Paper 6982},
  year = {2006},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=13816}
}

TY  - CPAPER
TI  - Loudspeaker and Room Response Modeling with Psychoacoustic Warping, Linear Prediction, and Parametric Filters
AU  - Bharitkar, Sunil
AU  - Kyriakakis, Chris
T2  - AES Convention 121
M1  - Paper 6982
PY  - 2006
DA  - 2006/10/06
UR  - https://aes.org/publications/elibrary-page/?id=13816
PB  - Audio Engineering Society
LA  - en
AB  - Traditionally, room response modeling is performed to obtain lower order room impulse response models for real-time applications. These models can be FIR or IIR, and maybe either linear-phase or minimum-phase. In this paper, we present an approach to model room responses using linear predictive coding (LPC) and parametric filters designed in the frequency warped domain. Frequency warping to the psychoacoustic Bark scale allows significant lower filter order designs. Within this context, the LPC model utilizes a significantly lower number of poles to model room resonances at low frequencies in the warped domain. The relatively low-order LPC pole locations and gains are then used to determine the center frequencies, the gain, and Q of a parametric filter bank. Gain and Q optimization of the parametric filter bank is performed to match the parametric filter spectrum to the LPC spectrum. Subsequently, the second-order poles and zeros of the parametric filter bank are directly unwarped back into the linear domain for low-complexity real-time applications. The results show that warping lowers the computational requirements for determining the roots as the density of the roots and the number of roots of the LPC polynomial is substantially reduced. Furthermore, results from using simply 4-6 parametric filter banks, modeled from the LPC spectrum, below 400 Hz show significant equalization.
ER  -