An Efficient Temporary Deepfake Location Approach Based Embeddings for Partially Spoofed Audio Detection

Partially spoofed audio detection is a challenging task, lying in the need to accurately locate the authenticity of audio at the frame level. To address this issue, we propose a fine-grained partially spoofed audio detection method, namely Temporal Deepfake Location (TDL), which can effectively capture information of both features and locations. Specifically, our approach involves two novel parts: embedding similarity module and temporal convolution operation. To enhance the identification between the real and fake features, the embedding similarity module is designed to generate an embedding space that can separate the real frames from fake frames. To effectively concentrate on the position information, temporal convolution operation is proposed to calculate the frame-specific similarities among neighboring frames, and dynamically select informative neighbors to convolution. Extensive experiments show that our method outperform baseline models in ASVspoof2019 Partial Spoof dataset and demonstrate superior performance even in the crossdataset scenario. The code is released online.Comment: Submitted to ICASSP 202

FSD: An Initial Chinese Dataset for Fake Song Detection

Singing voice synthesis and singing voice conversion have significantly advanced, revolutionizing musical experiences. However, the rise of "Deepfake Songs" generated by these technologies raises concerns about authenticity. Unlike Audio DeepFake Detection (ADD), the field of song deepfake detection lacks specialized datasets or methods for song authenticity verification. In this paper, we initially construct a Chinese Fake Song Detection (FSD) dataset to investigate the field of song deepfake detection. The fake songs in the FSD dataset are generated by five state-of-the-art singing voice synthesis and singing voice conversion methods. Our initial experiments on FSD revealed the ineffectiveness of existing speech-trained ADD models for the task of song deepFake detection. Thus, we employ the FSD dataset for the training of ADD models. We subsequently evaluate these models under two scenarios: one with the original songs and another with separated vocal tracks. Experiment results show that song-trained ADD models exhibit a 38.58% reduction in average equal error rate compared to speech-trained ADD models on the FSD test set.Comment: Submitted to ICASSP 202

Full-Field Vibration Measurements by Using High-Speed Two-Dimensional Digital Image Correlation

This work developed a method that uses a single monochrome high-speed camera without sacrificing the spatial resolution to measure both in-plane and out-of-plane full-field vibrations. By using the high-speed camera and a two-dimensional digital image correlation (2D-DIC) algorithm, the method first extracts the out-of-plane displacement field from the measured virtual in-plane strains. Then it retrieves the in-plane displacement field after eliminating the out-of-plane motion-induced virtual component. For validation, in-plane and out-of-plane translation tests and single-frequency vibration experiments were carried out. The measurement results show good agreement with the reference values, indicating the effectiveness of the proposed high-speed 2D-DIC (HS-2D-DIC). Further, the natural frequencies and mode shapes of a rectangular cantilever panel were also measured successfully, exhibiting the method’s effectiveness in practical applications. Since the HS-2D-DIC requires only a single monochrome camera, no complex optical setup, and no complicated calibration process, the method can be developed as a competitive tool for full-field vibration characterizations

New Understanding on Photocatalytic Mechanism of Nitrogen-Doped Graphene Quantum Dots-Decorated BiVO4 Nanojunction Photocatalysts

Bismuth vanadate (BiVO4) is a promising candidate as a visible-light-driven photocatalyst in the aspect of practical applications. To investigate the origin of active species from BiVO4 and understand the influence of the variations of the photocatalytic process, comparative studies on zero-dimensional nitrogen-doped graphene quantum dot (NGQD)-decorated BiVO4 have been carried out for methylene blue photodegradation. It was found that the hydroxyl group-rich NGQD surface and the established heterojunction structure between NGQDs and BiVO4 were greatly beneficial for the conversion of the MOH radical. With NGQD decoration, the dominant oxidant species for NGQDs/BiVO4 were confirmed to be MOH and H2O2, rather than holes originating from the valence band of unmodified BiVO4. The synergistic photocatalytic mechanism with respect to the interfacial charge transport and the conversion of active species was proposed. The achievement of the controllable active species significantly altering the activity may be applied for different photocatalytic reactions