Recoverable Identifier

arXiv:2604.19209 · detector doi_compliance · incontrovertible · 2026-05-20 03:14:28.827219+00:00

advisory doi_compliance recoverable_identifier

DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1109/ICASSP.2018.8462015.Waldemar) was visible in the surrounding text but could not be confirmed against doi.org as printed.

Paper page Integrity report arXiv Try DOI

Evidence text

Learning filterbanks from raw speech for phone recognition, in: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5509–5513. doi:10.1109/ICASSP.2018. 8462015. Waldemar Maciejko Page 10 of 10 Audio Spoof Detection with GaborNet Figure 1:Three examples of sinc functions at the training stage across three epochs:0𝑡ℎ,50 𝑡ℎ and100 𝑡ℎ. The top row presents the characteristics in the time domain, and the bottom row presents them in the frequency domain. Figure 2:Three examples of Gabor filters at the training stage across three epochs:0𝑡ℎ,50 𝑡ℎ and100 𝑡ℎ. The top row shows characteristics in the time domain, and the bottom row presents characteristics in the frequency domain. Figure 3:The schema of Filter Map Scaling applied in Gabor RawNet2. Waldemar Maciejko Page 11 of 10 Audio Spoof Detection with GaborNet Figure 4:The schema of the Top K-Pooling layer applied in RawGAT-ST separately to time, frequency and fusion domains. Figure 5:Comparison of training curves of investigated architectures. Waldemar Maciejko Page 12 of 10

Evidence payload

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  "printed_excerpt": "Learning filterbanks from raw speech for phone recognition, in: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5509\u20135513. doi:10.1109/ICASSP.2018. 8462015. Waldemar Maciejko Page 10 of 10 Audio S",
  "reconstructed_doi": "10.1109/ICASSP.2018.8462015.Waldemar",
  "ref_index": 13,
  "resolved_title": null,
  "verdict_class": "incontrovertible"
}