Partial Spectral Flatness Measure for Tonality Estimation in a Filter Bank-Based Psychoacoustic Model for Perceptual Audio Coding

Taghipour A, Jaikumar MC, Edler B, Stahl H. Partial Spectral Flatness Measure for Tonality Estimation in a Filter Bank-Based Psychoacoustic Model for Perceptual Audio Coding. In: AES Convention 134. Audio Engineering Society; 2013. Paper 8824. Available from: https://aes.org/publications/elibrary-page/?id=16725

@inproceedings{Taghipour2013_16725,
  author = {Taghipour, Armin and Jaikumar, Maneesh Chandra and Edler, Bernd and Stahl, Holger},
  title = {{Partial Spectral Flatness Measure for Tonality Estimation in a Filter Bank-Based Psychoacoustic Model for Perceptual Audio Coding}},
  booktitle = {AES Convention 134},
  note = {Paper 8824},
  year = {2013},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=16725}
}

TY  - CPAPER
TI  - Partial Spectral Flatness Measure for Tonality Estimation in a Filter Bank-Based Psychoacoustic Model for Perceptual Audio Coding
AU  - Taghipour, Armin
AU  - Jaikumar, Maneesh Chandra
AU  - Edler, Bernd
AU  - Stahl, Holger
T2  - AES Convention 134
M1  - Paper 8824
PY  - 2013
DA  - 2013/05/06
UR  - https://aes.org/publications/elibrary-page/?id=16725
PB  - Audio Engineering Society
LA  - en
AB  - Perceptual audio codecs use psychoacoustic models for irrelevancy reduction by exploiting masking effects in the human auditory system. In masking, the tonality of the masker plays an important role and therefore should be evaluated in the psychoacoustic model. In this study a partial Spectral Flatness Measure (SFM) is applied to a filter bank-based psychoacoustic model to estimate tonality. The Infinite Impulse Response (IIR) band-pass filters are designed to take into account the spreading in simultaneous masking. Tonality estimation is adapted to temporal and spectral resolution of the auditory system. Employing subjective audio coding preference tests, the Partial SFM is compared with prediction-based tonality estimation.
ER  -