Design, Modelling, and Control of a Reconfigurable Rotary Series Elastic Actuator with Nonlinear Stiffness for Assistive Robots

In assistive robots, compliant actuator is a key component in establishing safe and satisfactory physical human-robot interaction (pHRI). The performance of compliant actuators largely depends on the stiffness of the elastic element. Generally, low stiffness is desirable to achieve low impedance, high fidelity of force control and safe pHRI, while high stiffness is required to ensure sufficient force bandwidth and output force. These requirements, however, are contradictory and often vary according to different tasks and conditions. In order to address the contradiction of stiffness selection and improve adaptability to different applications, we develop a reconfigurable rotary series elastic actuator with nonlinear stiffness (RRSEAns) for assistive robots. In this paper, an accurate model of the reconfigurable rotary series elastic element (RSEE) is presented and the adjusting principles are investigated, followed by detailed analysis and experimental validation. The RRSEAns can provide a wide range of stiffness from 0.095 Nm/deg to 2.33 Nm/deg, and different stiffness profiles can be yielded with respect to different configuration of the reconfigurable RSEE. The overall performance of the RRSEAns is verified by experiments on frequency response, torque control and pHRI, which is adequate for most applications in assistive robots. Specifically, the root-mean-square (RMS) error of the interaction torque results as low as 0.07 Nm in transparent/human-in-charge mode, demonstrating the advantages of the RRSEAns in pHRI

Low-rank Adaptation Method for Wav2vec2-based Fake Audio Detection

Self-supervised speech models are a rapidly developing research topic in fake audio detection. Many pre-trained models can serve as feature extractors, learning richer and higher-level speech features. However,when fine-tuning pre-trained models, there is often a challenge of excessively long training times and high memory consumption, and complete fine-tuning is also very expensive. To alleviate this problem, we apply low-rank adaptation(LoRA) to the wav2vec2 model, freezing the pre-trained model weights and injecting a trainable rank-decomposition matrix into each layer of the transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared with fine-tuning with Adam on the wav2vec2 model containing 317M training parameters, LoRA achieved similar performance by reducing the number of trainable parameters by 198 times.Comment: 6page

System Fingerprint Recognition for Deepfake Audio: An Initial Dataset and Investigation

The malicious use of deep speech synthesis models may pose significant threat to society. Therefore, many studies have emerged to detect the so-called ``deepfake audio". However, these studies focus on the binary detection of real audio and fake audio. For some realistic application scenarios, it is needed to know what tool or model generated the deepfake audio. This raises a question: Can we recognize the system fingerprints of deepfake audio? Therefore, in this paper, we propose a deepfake audio dataset for system fingerprint recognition (SFR) and conduct an initial investigation. We collected the dataset from five speech synthesis systems using the latest state-of-the-art deep learning technologies, including both clean and compressed sets. In addition, to facilitate the further development of system fingerprint recognition methods, we give researchers some benchmarks that can be compared, and research findings. The dataset will be publicly available.Comment: 12 pages, 3 figures. arXiv admin note: text overlap with arXiv:2208.0964