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- W4311458890 abstract "Large number of audio recordings are used in law enforcement and litigation procedures, and it also brings security issues such as the identification of the audio source. This paper mainly studies the problem of source identification (device detection). We proposed an audio source identification framework based on an improved residual network model that introduces a character category output, which will help to improve the identification accuracy for the special case of cross speaker. Experiments show that this audio source identification framework based on residual network has achieved good results under the condition of non-target recognition task, with the highest accuracy rate reaching above 98%, which outperforms the current audio source identification algorithm." @default.
- W4311458890 created "2022-12-26" @default.
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- W4311458890 date "2022-12-08" @default.
- W4311458890 modified "2023-09-27" @default.
- W4311458890 title "Audio source identification based on residual network" @default.
- W4311458890 doi "https://doi.org/10.1117/12.2653493" @default.
- W4311458890 hasPublicationYear "2022" @default.
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