一种小波域音频信息隐藏方法

提出了一种基于量化的小波域音频隐藏算法，将保密语音隐藏到载体音频中．为提高隐藏重和保密语音传输的安全性，对保密语音进行了小波域压缩编码和m序列的扩频调制，生成待隐藏的比特序列；通过量化方法，将编码和调制后的保密语音隐藏到载体音频的小波系数中；保密语音的恢复过程不需要使用原始音频、仿真结果表明，隐藏有保密语音的载体音频听觉质量没有明显下降，提取的保密语音感知质量较好；该算法对重量化、加噪、低通滤波等攻击均有良好的鲁棒性．     

Visual Speech Recognition with Lightweight Psychologically Motivated Gabor Features

Extraction of relevant lip features is of continuing interest in the visual speech domain. Using end-to-end feature extraction can produce good results, but at the cost of the results being difficult for humans to comprehend and relate to. We present a new, lightweight feature extraction approach, motivated by human-centric glimpse-based psychological research into facial barcodes, and demonstrate that these simple, easy to extract 3D geometric features (produced using Gabor-based image patches), can successfully be used for speech recognition with LSTM-based machine learning. This approach can successfully extract low dimensionality lip parameters with a minimum of processing. One key difference between using these Gabor-based features and using other features such as traditional DCT, or the current fashion for CNN features is that these are human-centric features that can be visualised and analysed by humans. This means that it is easier to explain and visualise the results. They can also be used for reliable speech recognition, as demonstrated using the Grid corpus. Results for overlapping speakers using our lightweight system gave a recognition rate of over 82%, which compares well to less explainable features in the literature.     

Reliability of Speaking and Maximum Voice Range Measures in Screening for Dysphonia

Speech range profile (SRP) is a graphical display of frequency-intensity occurring interactions during functional speech activity. Few studies have suggested the potential clinical applications of SRP. However, these studies are limited to qualitative case comparisons and vocally healthy participants. The present study aimed to examine the effects of voice disorders on speaking and maximum voice ranges in a group of vocally untrained women. It also aimed to examine whether voice limit measures derived from SRP were as sensitive as those derived from voice range profile (VRP) in distinguishing dysphonic from healthy voices. Ninety dysphonic women with laryngeal pathologies and 35 women with normal voices, who served as controls, participated in this study. Each subject recorded a VRP for her physiological vocal limits. In addition, each subject read aloud the "North Wind and the Sun" passage to record SRP. All the recordings were captured and analyzed by Soundswell's computerized real-time phonetogram Phog 1.0 (Hitech Development AB, T?by, Sweden). The SRPs and the VRPs were compared between the two groups of subjects. Univariate analysis results demonstrated that individual SRP measures were less sensitive than the corresponding VRP measures in discriminating dysphonic from normal voices. However, stepwise logistic regression analyses revealed that the combination of only two SRP measures was almost as effective as a combination of three VRP measures in predicting the presence of dysphonia (overall prediction accuracy: 93.6% for SRP vs 96.0% for VRP). These results suggest that in a busy clinic where quick voice screening results are desirable, SRP can be an acceptable alternate procedure to VRP.     

Speaker Recognition Using Constrained Convolutional Neural Networks in Emotional Speech

Speaker recognition is an important classification task, which can be solved using several approaches. Although building a speaker recognition model on a closed set of speakers under neutral speaking conditions is a well-researched task and there are solutions that provide excellent performance, the classification accuracy of developed models significantly decreases when applying them to emotional speech or in the presence of interference. Furthermore, deep models may require a large number of parameters, so constrained solutions are desirable in order to implement them on edge devices in the Internet of Things systems for real-time detection. The aim of this paper is to propose a simple and constrained convolutional neural network for speaker recognition tasks and to examine its robustness for recognition in emotional speech conditions. We examine three quantization methods for developing a constrained network: floating-point eight format, ternary scalar quantization, and binary scalar quantization. The results are demonstrated on the recently recorded SEAC dataset.     

一种复杂车辆图像中的多车牌定位方法

针对复杂背景中多个车牌的定位问题,提出一种新的定位方法.该方法综合利用边缘检测、连通域分析、倾斜矫正等多种方法,解决了复杂背景中定位难的问题.能够准确定位杂乱背景中的车牌,对天气、光照变化、车牌在图像中的移动和旋转等,具有良好的适应能力.该方法为后续的字符分割和字符识别提供旋转角度、字符区域定位信息.     

Faithful compact quantum group actions on connected compact metrizable spaces

We construct faithful actions of quantum permutation groups on connected compact metrizable spaces. This disproves a conjecture of Goswami.