References¶
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[37] G. Chen, Y. Wu, S. Liu, T. Liu, X. Du, and F. Wei, "WavMark: Watermarking for Audio Generation," arXiv preprint, arXiv:2308.12770, 2023. arXiv:2308.12770
[38] H. Liu, M. Guo, Z. Jiang, L. Wang, and N. Z. Gong, "AudioMarkBench: Benchmarking Robustness of Audio Watermarking," in Advances in Neural Information Processing Systems 37 (NeurIPS Datasets and Benchmarks), 2024. arXiv:2406.06979
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[44] P. Li, X. Zhang, J. Xiao, and J. Wang, "IDEAW: Robust Neural Audio Watermarking with Invertible Dual-Embedding," in Proceedings of EMNLP 2024, 2024. doi:10.18653/v1/2024.emnlp-main.258; arXiv:2409.19627.
Software resources¶
The following open-source projects served as references for, or are wrapped by, individual components.
| Ref. | Project | Scope |
|---|---|---|
| [S1] | audio-watermarking | Audio watermarking and steganography implementations |
| [S2] | audio-steganography-algorithms | Audio steganography algorithm examples |
| [S3] | pysepm | Objective speech enhancement and quality measures |
| [S4] | PESQ | Python wrapper for ITU-T P.862 PESQ |
| [S5] | pystoi | STOI implementation |
| [S6] | SRMRpy | SRMR implementation |
| [S7] | mel_cepstral_distance | Mel-cepstral distance implementation |
| [S8] | semetrics | Speech enhancement measures |
| [S9] | py-intelligibility | wSTMI and STGI intelligibility measures |
| [S10] | speechmetrics | Speech and audio evaluation measures |
| [S11] | sigsep-mus-eval | BSSEval v4 |
Additional metric sources¶
[45] J. Jensen and C. H. Taal, “An Algorithm for Predicting the Intelligibility of Speech Masked by Modulated Noise Maskers,” IEEE/ACM TASLP, 24(11), 2009–2022, 2016. DOI.
[46] M. Chinen et al., “ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric,” QoMEX, 2020. Paper, implementation.
Additional method sources¶
[47] X.-Y. Wang and H. Zhao, "A Novel Synchronization Invariant Audio Watermarking Scheme Based on DWT and DCT," IEEE Transactions on Signal Processing, vol. 54, no. 12, pp. 4835-4840, 2006. doi:10.1109/TSP.2006.881258
[48] K. Khaldi and A.-O. Boudraa, "Audio Watermarking Via EMD," IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, no. 3, pp. 675-680, 2013. doi:10.1109/TASL.2012.2227733
[49] D. M. Thodi and J. J. Rodríguez, "Expansion Embedding Techniques for Reversible Watermarking," IEEE Transactions on Image Processing, vol. 16, no. 3, pp. 721-730, 2007. doi:10.1109/TIP.2006.891046
[50] A. Nishimura, "Reversible Audio Data Hiding Using Linear Prediction and Error Expansion," in Proceedings of the Seventh International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP), pp. 318-321, 2011. doi:10.1109/IIHMSP.2011.76
Source use¶
Method descriptions combine the cited literature with the linked TAF implementation. A citation provides scientific context, not evidence that TAF reproduces every detail or numerical result. References [1] and [7] are a review and a textbook. Reference [49] defines prediction-error expansion for images; [50] applies it to audio, which is the setting of the PEE method. The full texts of [47] and [48] were not available when the Sync-DWT-DCT and EMD methods were written. Their descriptions follow the abstracts and the accounts of the schemes in later studies (for [47], arXiv:1704.02754); carrier coefficients, quantisation rules and step sizes are TAF's own choices. Reference [8] describes the Python wrapper; the underlying PESQ definition is ITU-T P.862.
Accessible primary texts consulted for the concise descriptions include AudioMarkBench, §3 and Appendix A.2, AudioSeal, WavMark, FGAS, improved phase coding, MOSNet, STGI, and Hu and Loizou’s objective-measure study.