Effects of Convolutional Autoencoder Bottleneck Width on StarGAN-based Singing Technique Conversion

Singing technique conversion (STC) refers to the task of converting from one voice technique to another while leaving the original singer identity, melody, and linguistic components intact. Previous STC studies, as well as singing voice conversion research in general, have utilized convolutional autoencoders (CAEs) for conversion, but how the bottleneck width of the CAE affects the synthesis quality has not been thoroughly evaluated. To this end, we constructed a GAN-based multi-domain STC system which took advantage of the WORLD vocoder representation and the CAE architecture. We varied the bottleneck width of the CAE, and evaluated the conversion results subjectively. The model was trained on a Mandarin dataset which features four singers and four singing techniques: the chest voice, the falsetto, the raspy voice, and the whistle voice. The results show that a wider bottleneck corresponds to better articulation clarity but does not necessarily lead to higher likeness to the target technique. Among the four techniques, we also found that the whistle voice is the easiest target for conversion, while the other three techniques as a source produce more convincing conversion results than the whistle.Comment: The original edition of this paper will be published in the CMMR 2023 Proceedings. This ArXiv publication is a cop

Traditional Chinese Medicine Diagnosis “ Yang-Xu Zheng

Pathogenesis of sepsis includes complex interaction between pathogen activities and host response, manifesting highly variable signs and symptoms, possibly delaying diagnosis and timely life-saving interventions. This study applies traditional Chinese medicine (TCM) Zheng diagnosis in patients with severe sepsis and septic shock to evaluate its adaptability and use as an early predictor of sepsis mortality. Three-year prospective observational study enrolled 126 septic patients. TCM Zheng diagnosis, Acute Physiology and Chronic Health Evaluation (APACHE) II score, and blood samples for host response cytokines measurement (tumor necrosis factor-α, Interleukin-6, Interleukin-8, Interleukin-10, Interleukin-18) were collected within 24 hours after admission to Intensive Care Unit. Main outcome was 28-day mortality; multivariate logistic regression analysis served to determine predictive variables of the sepsis mortality. APACHE II score, frequency of Nutrient-phase heat, and Qi-Xu and Yang-Xu Zhengs were significantly higher in nonsurvivors. The multivariate logistic regression analysis identified Yang-Xu Zheng as the outcome predictor. APACHE II score and levels of five host response cytokines between patients with and without Yang-Xu Zheng revealed significant differences. Furthermore, cool extremities and weak pulse, both diagnostic signs of Yang-Xu Zheng, were also proven independent predictors of sepsis mortality. TCM diagnosis “Yang-Xu Zheng” may provide a new mortality predictor for septic patients

Haptic identification by ELM-controlled uncertain manipulator

This paper presents an extreme learning machine (ELM) based control scheme for uncertain robot manipulators to perform haptic identification. ELM is used to compensate for the unknown nonlinearity in the manipulator dynamics. The ELM enhanced controller ensures that the closed-loop controlled manipulator follows a specified reference model, in which the reference point as well as the feedforward force is adjusted after each trial for haptic identification of geometry and stiffness of an unknown object. A neural learning law is designed to ensure finite-time convergence of the neural weight learning, such that exact matching with the reference model can be achieved after the initial iteration. The usefulness of the proposed method is tested and demonstrated by extensive simulation studies. Index Terms—Extreme learning machine; haptic identification; adaptive control; robot manipulator