An Integrated Framework for Two-pass Personalized Voice Trigger

In this paper, we present the XMUSPEECH system for Task 1 of 2020 Personalized Voice Trigger Challenge (PVTC2020). Task 1 is a joint wake-up word detection with speaker verification on close talking data. The whole system consists of a keyword spotting (KWS) sub-system and a speaker verification (SV) sub-system. For the KWS system, we applied a Temporal Depthwise Separable Convolution Residual Network (TDSC-ResNet) to improve the system's performance. For the SV system, we proposed a multi-task learning network, where phonetic branch is trained with the character label of the utterance, and speaker branch is trained with the label of the speaker. Phonetic branch is optimized with connectionist temporal classification (CTC) loss, which is treated as an auxiliary module for speaker branch. Experiments show that our system gets significant improvements compared with baseline system.

Crop Height and Plot Estimation from Unmanned Aerial Vehicles using 3D LiDAR

In this paper, we present techniques to measure crop heights using a 3D LiDAR mounted on an Unmanned Aerial Vehicle (UAV). Knowing the height of plants is crucial to monitor their overall health and growth cycles, especially for high-throughput plant phenotyping. We present a methodology for extracting plant heights from 3D LiDAR point clouds, specifically focusing on row-crop environments. The key steps in our algorithm are clustering of LiDAR points to semi-automatically detect plots, local ground plane estimation, and height estimation. The plot detection uses a k--means clustering algorithm followed by a voting scheme to find the bounding boxes of individual plots. We conducted a series of experiments in controlled and natural settings. Our algorithm was able to estimate the plant heights in a field with 112 plots within +-5.36%. This is the first such dataset for 3D LiDAR from an airborne robot over a wheat field. The developed code can be found on the GitHub repository located at https://github.com/hsd1121/PointCloudProcessing.

Bottleneck Supervised U-Net for Pixel-wise Liver and Tumor Segmentation

Convolutional neural network (CNN) has been widely used for image processing tasks.In this paper we design a bottleneck supervised U-Net model and apply it to liver and tumor segmentation. Taking an image as input, the model outputs segmented images of the same size, each pixel of which takes value from 1 to K where K is the number of classes to be segmented. The innovations of this paper are two-fold: first we design a novel U-Net structure which include dense block and inception block as the base U-Net; second we design a double U-Net architecture based on the base U-Net and includes an encoding U-Net and a segmentation U-Net. The encoding U-Net is first trained to encode the labels, then the encodings are used to supervise the bottleneck of the segmentation U-Net. While training the segmentation U-Net, a weighted average of dice loss(for the final output) and MSE loss(for the bottleneck) is used as the overall loss function. This approach can help retain the hidden features of input images. The model is applied to a liver tumor 3D CT scan dataset to conduct liver and tumor segmentation sequentially. Experimental results indicate bottleneck supervised U-Net can accomplish segmentation tasks effectively with better performance in controlling shape distortion, reducing false positive and false negative, besides accelerating convergence. Besides, this model has good generalization for further improvement.