# Pyramid Feature Attention Network for Speech Resampling Detection
## 1 Introduction  
In the rapidly evolving field of digital speech, particularly in the context of digital avatars, the ease of audio editing and modification through software (such as Audacity 3.4.2 [1], developed by Audacity Team, sourced from Carnegie Mellon University, Pittsburgh, USA; Adobe Audition 23.5 [2], developed by Adobe Systems, sourced from San Jose, California, USA; and GoldWave 6.80 [3], developed by GoldWave Inc., sourced from St. John's, Newfoundland and Labrador, Canada) raises critical concerns for security and privacy. The undetectable manipulation of digital speech avatars poses substantial threats to judicial processes, political fields, and social security. Contemporary speech forensics techniques are pivotal in ensuring the integrity of digital avatars and focus on detecting tampering facilitated by audio editing software, such as deletion, insertion, copy and move, splicing, resampling and recompression of audio clips [4; 5; 6; 7]. It is worth noting that in the field of speech content forensics, there are more forensic methods for speech deletion, copy and move, splicing, and other tampering approaches [8; 9; 10], while there are relatively few methods for speech resampling forensics, and these tampering means are often accompanied by resampling operations. Thus, the study of speech resampling detection has significance in complex forensics environments, which will provide comprehensive tools and methods for forensics to ensure the authenticity and integrity of audio. Speech resampling refers to converting the original sampling frequency of speech into a new sampling frequency. The bulk of the existing detection methods for speech resampling are inspired by detection algorithms of digital image resampling [11; 12]. According to the fact that the resampling process will introduce periodic information into the original speech, [PERSON] et al. [13] proposed an audio resampling detection method based on expectation maximization (EM), which uses the spectral statistical moments of fixed sample points as input features. At the same time, the audio amplitude histogram is introduced to approximate the audio signal distribution more accurately. However, this algorithm can only detect resampled speech under linear interpolation. For this reason, [PERSON] et al. [14] improved the algorithm of [PERSON] et al. [13]. The EM algorithm was used to simultaneously estimate a set of periodic sample points with domain correlation and specific correlation coefficients, and obtain the normalized probability vector of all sample points to observe whether it exhibits periodicity. The method is able to detect resampled speech using different interpolation methods. The algorithms in [13; 14] require highly accurate selection of initial parameters to reduce the impact of noise. Factors such as noise and signal distortion can lead to lower detection accuracy in speech down-sampling. [PERSON] et al. [15] detected resampling by analyzing the interpolation characteristics of resampled speech and studying the singular values of the signal formed by the sampling points. Specifically, after the singular value decomposition of the speech signal, a set of singular values and the corresponding left and right singular signals are obtained, and the variation in the speech signal is indicated by counting the average number of zero singular values. [PERSON] et al. [16], referred to the image forensics method proposed by [PERSON] et al. [17], that an image would show periodic domain correlation after resampling. They found that after resampling an audio signal through the second-order difference, the interval between its zero values is periodic, and this feature was applied to nonlinear interpolation. However, different thresholds are selected by this method for different resampling factors and interpolation kernels, so it has limitations in practical application. [PERSON] et al.