End-to-end Online Speaker Diarization with Target Speaker Tracking

This paper proposes an online target speaker voice activity detection system for speaker diarization tasks, which does not require a priori knowledge from the clustering-based diarization system to obtain the target speaker embeddings. By adapting the conventional target speaker voice activity detection for real-time operation, this framework can identify speaker activities using self-generated embeddings, resulting in consistent performance without permutation inconsistencies in the inference phase. During the inference process, we employ a front-end model to extract the frame-level speaker embeddings for each coming block of a signal. Next, we predict the detection state of each speaker based on these frame-level speaker embeddings and the previously estimated target speaker embedding. Then, the target speaker embeddings are updated by aggregating these frame-level speaker embeddings according to the predictions in the current block. Our model predicts the results for each block and updates the target speakers' embeddings until reaching the end of the signal. Experimental results show that the proposed method outperforms the offline clustering-based diarization system on the DIHARD III and AliMeeting datasets. The proposed method is further extended to multi-channel data, which achieves similar performance with the state-of-the-art offline diarization systems.Comment: Submitted to IEEE/ACM Transactions on Audio, Speech, and Language Processin

The Role of Oceanic Processes in the Initiation of Boreal Winter Intraseasonal Oscillations over the Indian Ocean

Observational analyses and a hierarchy of ocean general circulation model (OGCM) experiments were performed to understand the influence of oceanic processes on the warm sea surface temperature anomalies (SSTAs) prior to the convection initiation of boreal winter intraseasonal oscillations (ISOs), including the Madden-Julian Oscillation (MJO), in the equatorial Indian Ocean. We found 39 strong ISOs that passed over the Indian Ocean Warm Pool region during the November-April season of the 2001-2012 period. 17/39 ISO events initiated in the Seychelles-Chagos Thermocline Ridge (SCTR) before propagating eastward; the remaining events initiated in the southern Arabian Sea (6) or Warm Pool (16) regions. The SCTR event set was notable in that it contained more global-scale MJOs (71-76%), as defined by the RMM and OMI indices, than the WP events (25-44%). Additionally, ~24% (44%) of the SCTR (Warm Pool) events were preceded by strong oceanic process-induced SSTAs of similar magnitude to those of shortwave radiative and turbulent heat fluxes. The Arabian Sea events, however, were not associated with statistically significant SSTA signals prior to convection. Based on a mixed layer heat budget analysis, entrainment and upwelling reduction were the dominant oceanic processes contributing to the warming, in contrast with boreal summer, when horizontal advection dominated. We examined several case studies, including primary MJO events, where oceanic Rossby waves were associated with the entrainment and upwelling reduction. Two simple atmospheric boundary layer convergence models revealed that the SSTAs contributed at least half of the total convergence and suggested that the ocean dynamical effect was responsible for the majority of SSTA-forced convergence for those case studies. These results underscore the need for climate prediction models to accurately represent the ocean structure and processes to include the effects of oceanic predictors

Analysis on Drill String Vibration Signal of Stick Slip and Bit Bouncing

In drilling engineering, drill string premature damage are frequently observed, resulting in drilling cost and cycle prolonged. Researches show that, the common phenomenon of drill string vibration is the main cause of premature damage, thus more and more attention is paid to research on drill string vibration. Real time monitor and analysis on drill string vibration has important implications for vibration control, complex prevention, downhole condition prediction and drilling parameter optimization. Cases of vibration signal of stick slip and bit bouncing are collected and analyzed with Fourier transform, and frequency spectrum characteristic of vibration signal is obtained. The results show that, the vibration signal can reflect bottom hole condition of drill string, thus to predict premature damage of drill string and decrease drilling cost.Key words: Drill string vibration; Stick slip; Bit bouncing; Signal characteristi

Examining the Supply Chain Management Models for Agricultural Products Under the Context of E-Commerce

Agricultural products market changes constantly along with the thriving of e-commerce and agricultural products e-commerce keeps growing as an innovative industry; however, there are still many loopholes in the management of the supply chain from beginning to end. In order to effectively address these issues, this paper utilizes the dynamic requirement forecasting method based on SVM (support vector machine) to identify and fit the secular trend in and potential cyclical fluctuation factors for the market requirements for agricultural products. The supply chain coordination decision center is established by integrating the collaborative supply management component and other components. XML technology and CORBA technology are adopted to construct the integrated management model of agricultural products supply chain in e-commerce environment. For its relatively high management level, the model established can promote both agricultural consumption and agricultural economic output, strengthen the competitiveness of enterprises in agricultural products market and realize maximization of profit targets

No-reference Point Cloud Geometry Quality Assessment Based on Pairwise Rank Learning

Objective geometry quality assessment of point clouds is essential to evaluate the performance of a wide range of point cloud-based solutions, such as denoising, simplification, reconstruction, and watermarking. Existing point cloud quality assessment (PCQA) methods dedicate to assigning absolute quality scores to distorted point clouds. Their performance is strongly reliant on the quality and quantity of subjective ground-truth scores for training, which are challenging to gather and have been shown to be imprecise, biased, and inconsistent. Furthermore, the majority of existing objective geometry quality assessment approaches are carried out by full-reference traditional metrics. So far, point-based no-reference geometry-only quality assessment techniques have not yet been investigated. This paper presents PRL-GQA, the first pairwise learning framework for no-reference geometry-only quality assessment of point clouds, to the best of our knowledge. The proposed PRL-GQA framework employs a siamese deep architecture, which takes as input a pair of point clouds and outputs their rank order. Each siamese architecture branch is a geometry quality assessment network (GQANet), which is designed to extract multi-scale quality-aware geometric features and output a quality index for the input point cloud. Then, based on the predicted quality indexes, a pairwise rank learning module is introduced to rank the relative quality of a pair of degraded point clouds.Extensive experiments demonstrate the effectiveness of the proposed PRL-GQA framework. Furthermore, the results also show that the fine-tuned no-reference GQANet performs competitively when compared to existing full-reference geometry quality assessment metrics