Multi-Band Generalized Harmonic Analysis (MGHA) and Its Fundamental Characteristics in Audio Signal Processing

Miura T, Muraoka T, Ifukube T. Multi-Band Generalized Harmonic Analysis (MGHA) and Its Fundamental Characteristics in Audio Signal Processing. In: AES Convention 126. Audio Engineering Society; 2009. Paper 7703. Available from: https://aes.org/publications/elibrary-page/?id=14899

@inproceedings{Miura2009_14899,
  author = {Miura, Takahiro and Muraoka, Teruo and Ifukube, Tohru},
  title = {{Multi-Band Generalized Harmonic Analysis (MGHA) and Its Fundamental Characteristics in Audio Signal Processing}},
  booktitle = {AES Convention 126},
  note = {Paper 7703},
  year = {2009},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=14899}
}

TY  - CPAPER
TI  - Multi-Band Generalized Harmonic Analysis (MGHA) and Its Fundamental Characteristics in Audio Signal Processing
AU  - Miura, Takahiro
AU  - Muraoka, Teruo
AU  - Ifukube, Tohru
T2  - AES Convention 126
M1  - Paper 7703
PY  - 2009
DA  - 2009/05/06
UR  - https://aes.org/publications/elibrary-page/?id=14899
PB  - Audio Engineering Society
LA  - en
AB  - One of the main problems in sound restoration of valuable historical recordings includes the noise reduction. We have been proposing and continuing to improve the noise reduction method utilized by inharmonic analysis such as GHA (Generalized Harmonic Analysis). Algorithm of GHA frequency extraction enables to extract arbitrary frequency components. In this report, we aimed at more accurate frequency identification from noisy signals to divide analyzed frequency section into multi-bands before analysis: this algorithm is named as Multi-band GHA (MGHA). The simulation of frequency analysis in noise-free condition indicated that MGHA is more effective than GHA for the extraction of low frequency components in the condition of both lower window length and amount of frequency components. However, excluding the case of both lower window length and amount of frequency components, GHA identifies frequency components more precisely. Furthermore the result of frequency analysis in condition with steady noise shows that MGHA can be more effectively applied to the case of short window length, many frequency components and low S/N.
ER  -