Regression and Classification for Direction-of-Arrival Estimation with Convolutional Recurrent Neural Networks

We present a novel learning-based approach to estimate the direction-of-arrival (DOA) of a sound source using a convolutional recurrent neural network (CRNN) trained via regression on synthetic data and Cartesian labels. We also describe an improved method to generate synthetic data to train the neural network using state-of-the-art sound propagation algorithms that model specular as well as diffuse reflections of sound. We compare our model against three other CRNNs trained using different formulations of the same problem: classification on categorical labels, and regression on spherical coordinate labels. In practice, our model achieves up to 43% decrease in angular error over prior methods. The use of diffuse reflection results in 34% and 41% reduction in angular prediction errors on LOCATA and SOFA datasets, respectively, over prior methods based on image-source methods. Our method results in an additional 3% error reduction over prior schemes that use classification based networks, and we use 36% fewer network parameters

Novel Broadband Amplifier for Mid-Infrared Semiconductor laser and applications in spectroscopy

An amplifier design for broadband Mid-IR buried-hetero (BH) structure epitaxial laser is presented, and external cavity design based on this amplifier is described. Spectroscopy results characterizing such single frequency lasers are demonstrated with whispering gallery mode CaF2 disc/ball, saturated absorption in hollow waveguide and direct chemical analysis in water

Synthetic Wave-Geometric Impulse Responses for Improved Speech Dereverberation

We present a novel approach to improve the performance of learning-based speech dereverberation using accurate synthetic datasets. Our approach is designed to recover the reverb-free signal from a reverberant speech signal. We show that accurately simulating the low-frequency components of Room Impulse Responses (RIRs) is important to achieving good dereverberation. We use the GWA dataset that consists of synthetic RIRs generated in a hybrid fashion: an accurate wave-based solver is used to simulate the lower frequencies and geometric ray tracing methods simulate the higher frequencies. We demonstrate that speech dereverberation models trained on hybrid synthetic RIRs outperform models trained on RIRs generated by prior geometric ray tracing methods on four real-world RIR datasets.Comment: Submitted to ICASSP 202

Study on Stranded Crowd Number Quantitative Model during Evacuation for University's Multifunctional Gymnasium

Abstract: This study, beginning with crowding situation caused by staff stranding in gymnasium, introduces theoretic basis of relative accidents, definite basic parameters including crowd flow rate, group flow and marginal sizes of evacuation channel, walking velocity of groups with different densities and predictive velocities of evacuation in different regions. Deduce and build stranded crow number quantitative model. By analyzing cases, calculate specific route and time of a Chinese university's multifunctional gymnasium in travel time method. Find evacuation bottle neck probably exist. Calculate specific stranding situation according to such model, find potential safety hazard and provides advice for improving stands exits. It can provide evaluation standards and references for designing, managing and transformation. It can also choose evacuation routes and make emergency plan

A New Image Similarity Metric for Improving Deformation Consistency in Graph-Based Groupwise Image Registration

Graph-based groupwise image registration (G-GIR) aims to register a group of input images accurately without any bias. In G-GIR, an image similarity metric (ISM) is used to construct a graph that links similar images with graph edges. From the graph, a group center image and the shortest paths linking it to all other images can be determined. The deformation field aligning each image to the group center image can be obtained by composing sub-deformation fields that come from registration of adjacent images along the corresponding shortest path. The majority of ISMs used in G-GIR are based on image intensity. Since image intensity can be ambiguous and is not directly related to deformation directions, inconsistency problem in the sub-deformation fields along the shortest paths can occur. The word "inconsistency" mentioned here refers to the directions of deformation vectors in the sub-deformation fields along each shortest path are significantly different or even opposite at corresponding locations. Such problem can make G-GIR inefficient and easily to be trapped in local minimum. In this paper, we propose a new ISM for G-GIR, by which the consistency in the sub-deformation fields along the shortest paths can be significantly improved. We evaluate our method in comparison with three state-of-the-art ISMs using a common G-GIR framework. The experimental results with both toy and real images show that our method significantly improves registration accuracy