A novel derivative-based approach for the automatic detection of time-reversed audio in the MPAI/IEEE-CAE ARP international standard

Bosi M, Zanini F, Spanio M, Russo A, Canazza S. A novel derivative-based approach for the automatic detection of time-reversed audio in the MPAI/IEEE-CAE ARP international standard. In: Convention Paper. Audio Engineering Society; 2024. Paper 10190. Available from: https://aes.org/publications/elibrary-page/?id=22693

@inproceedings{Bosi2024_22693,
  author = {Bosi, Marina and Zanini, Fabio and Spanio, Matteo and Russo, Alessandro and Canazza, Sergio},
  title = {{A novel derivative-based approach for the automatic detection of time-reversed audio in the MPAI/IEEE-CAE ARP international standard}},
  note = {Paper 10190},
  year = {2024},
  month = sep,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22693}
}

TY  - CPAPER
TI  - A novel derivative-based approach for the automatic detection of time-reversed audio in the MPAI/IEEE-CAE ARP international standard
AU  - Bosi, Marina
AU  - Zanini, Fabio
AU  - Spanio, Matteo
AU  - Russo, Alessandro
AU  - Canazza, Sergio
M1  - Paper 10190
PY  - 2024
DA  - 2024/09/27
UR  - https://aes.org/publications/elibrary-page/?id=22693
PB  - Audio Engineering Society
LA  - en
AB  - The Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Context-based Audio Enhancement (CAE) Audio Recording Preservation (ARP) standard provides the technical specifications for a comprehensive framework for digitizing and preserving analog audio, specifically focusing on documents recorded on open-reel tapes. This paper presents a novel envelope derivative-based method designed to be integrated into the ARP standard, for detecting reverse audio sections during the preservation process. The primary objective of this method is to automatically identify segments of audio recorded in reverse. Leveraging derivative-based signal processing algorithms, the system enhances its capability to detect and reverse such sections, thereby reducing errors during the preservation process. This feature not only aids in identifying and correcting errors but also enhances the efficiency of large-scale audio document archiving projects. The systems performance was evaluated using a diverse dataset that includes various musical genres and digitized tapes, demonstrating its strong potential and effectiveness across different types of audio content.
ER  -