Audio Source Verification Method Based on Structural Re-parameterization Network

Yingqiu Zhang, Da Luo
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Abstract

With the development of science and technology, the application of audio data in law enforcement and judicial fields is becoming increasingly widespread. Multimedia data such as audio recordings are prone to forgery, which brings great trouble to judicial fairness. When digital recordings are used as evidence, effective techniques are needed to ensure their reliability. For example, digital audio needs to verify its recording device. In this paper, we focus on the verification problem of audio source, i.e. determining whether a piece of audio recording comes from a given target device. We propose an audio source detection framework based on a structural re-parameterized network, and with a carefully designated loss function, the recognition accuracy is improved under the noise conditions. Experiments show the proposed method achieved a TPR of 99.89% and an FPR of 4.17%, which is superior to existing audio source detection methods.
基于结构重参数化网络的音频源验证方法
随着科学技术的发展,音频数据在执法和司法领域的应用日益广泛。录音等多媒体数据容易被伪造,给司法公正带来了很大的困扰。当数字录音被用作证据时,需要有效的技术来确保其可靠性。例如,数字音频需要验证其录制设备。本文主要研究音频源的验证问题,即确定一段音频是否来自给定的目标设备。我们提出了一种基于结构重参数化网络的音源检测框架,并通过精心指定的损失函数提高了噪声条件下的识别精度。实验表明,该方法的TPR为99.89%,FPR为4.17%,优于现有的音源检测方法。
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