This paper describes to our knowledge the first Chinese Braille speech synthesis system. The system consists of modules of Braille front-end processing, prosody prediction, and speech synthesis. The Braille front-end processing includes conversion from the common Braille to Pinyin, and a high-precision Chinese character prediction model. To achieve high precision prosody prediction under limited corpus conditions, we propose a prosody prediction model based on the RoBERTa pre-trained model, which achieves an accuracy of 94.42%. Finally, a real-time TTS system based on Tacotron2 and LPCNet is proposed. We modify Tacotron2, including introducing a forward attention mechanism and extending the autoregressive correlation step size to obtain more natural speech.

There are two sub-tasks implied in the weakly-supervised SED: audio tagging and event boundary detection. Current methods which combine multi-task learning with SED requires annotations both for these two sub-tasks. Since there are only annotations for audio tagging available in weakly-supervised SED, we design multiple branches with different learning purposes instead of pursuing multiple tasks. Similar to multiple tasks, multiple different learning purposes can also prevent the common feature which the multiple branches share from overfitting to any one of the learning purposes. We design these multiple different learning purposes based on combinations of different MIL strategies and different pooling methods. Experiments on the DCASE 2018 Task 4 dataset and the URBAN-SED dataset both show that our method achieves competitive performance.

We propose a simple but efficient method termed Guided Learning for weakly-labeled semi-supervised sound event detection (SED). There are two sub-targets implied in weakly-labeled SED: audio tagging and boundary detection. Instead of designing a single model by considering a trade-off between the two sub-targets, we design a teacher model aiming at audio tagging to guide a student model aiming at boundary detection to learn using the unlabeled data. The guidance is guaranteed by the audio tagging performance gap of the two models. In the meantime, the student model liberated from the trade-off is able to provide more excellent boundary detection results. We propose a principle to design such two models based on the relation between the temporal compression scale and the two sub-targets. We also propose an end-to-end semi-supervised learning process for these two models to enable their abilities to rise alternately. Experiments on the DCASE2018 Task4 dataset show that our approach achieves competitive performance.

In this article, a special decision surface for the weakly-supervised sound event detection (SED) and a disentangled feature (DF) for the multi-label problem in polyphonic SED are proposed. We approach SED as a multiple instance learning (MIL) problem and utilize a neural network framework with a pooling module to solve it. General MIL approaches include two kinds: the instance-level approaches and embedding-level approaches. We present a method of generating instance-level probabilities for the embedding level approaches which tend to perform better than the instance-level approaches in terms of bag-level classification but can not provide instance-level probabilities in current approaches. Moreover, we further propose a specialized decision surface (SDS) for the embedding-level attention pooling. We analyze and explained why an embedding-level attention module with SDS is better than other typical pooling modules from the perspective of the high-level feature space. As for the problem of the unbalanced dataset and the co-occurrence of multiple categories in the polyphonic event detection task, we propose a DF to reduce interference among categories, which optimizes the high-level feature space by disentangling it based on class-wise identifiable information and obtaining multiple different subspaces. Experiments on the dataset of DCASE 2018 Task 4 show that the proposed SDS and DF significantly improve the detection performance of the embedding-level MIL approach with an attention pooling module and outperform the first place system in the challenge by $\mathbf {6.6}$ percentage points.

The conversion of Chinese Characters into Braille faces the challenges of Braille word segmentation, pronunciation determination, and tone marking. In this paper, we present the CBConv service for automatic conversion of Chinese characters into Braille with high accuracy. The service supports two modes: real-time conversion of Chinese strings and asynchronous conversion of documents. To address the challenges described above, an end-to-end deep learning framework is proposed, which can perform pronunciation determination, word segmentation and tone marking simultaneously using a deep neural network. Accuracy evaluation and user study are conducted, which demonstrate the usability of the proposed system.

Most visual based obstacle detection methods usually detect obstacles on the whole image, which are influenced by complex background and various illuminations. This paper proposes a novel and simple obstacle detection method DLP based on analyzing Distortion of Laser Pattern. To reduce computation, a laser pattern with two cross lines is used to project onto the front ground. Firstly, a segmentation algorithm based on color and edge is proposed to extract laser pixels. Then, skeletons of laser pattern are quickly extracted using table scanning strategy to further reduce noise. Secondly, seed-filling is used to extract the region of skeletons, and then laser lines are detected by Hough and merged by region based rule. Finally, according to the distortion of laser pattern projected on different obstacles, we propose a hierarchical discriminant model, which can classify the front scene as free or having obstacles. And the obstacle can be further classified as raised or sunken obstacle, and ascending or descending stairs. Experimental results show the robustness and effectiveness of our DLP method in different scenes, including indoor and outdoor, day and night with 10f/s.

With the powerful development of pedestrian detection technique based on sliding-window and machine-learning, detection-based tracking systems have become increasingly popular. Most of these systems rely on existing static pedestrian detectors only despite the obvious potential motion information for people detection. This paper proposes a novel pedestrian detection framework fusing static and dynamic features. Motion cue is firstly used to detect potential pedestrian regions. Secondly, static detector scans potential regions to get candidate pedestrian detections. Final detection results are improved by removing false detections based on their motion distribution. The proposed framework significantly raises detection speed and detection performance. Static detector of pedestrian in this paper is trained by AdaBoost with simplified HOG feature (1HOG). Additionally, we introduce a detection-window-pyramid based scanning strategy for quickly extracting 1HOG features. The experimental results on several public data sets show the effectiveness of the proposed approach.

In this demonstration, a novel human-computer interaction device is proposed to realize finger touching for large display and mobile device control, without a touchscreen or a touch pad. In this method, interaction commands are input in a same way as traditional touchscreen and touchpad, which is convenient to develop applications for long-distance operation of display and mobile devices. An embedded module is designed to collect bone-conducted friction sound, acceleration and gyroscope sensor data, corresponding to the behavior and direction of interaction commands. For algorithm, modified MFCC and SVM are applied in sound processing and probability calculation.

Automatic detection of commercials in digital multimedia material is a challenging task with many applications. This paper presents a novel approach to fast commercial detection based on audio retrieval. It is based on the idea of segmenting energy envelope of audio into units, using only audio signal for matching on a commercial database. Fast searching and matching can be performed with high accuracy, by searching and by novel similarity function based on units. Experimental results show that 96.8% recall rate and 98.7% precision rate can be achieved under 0.125 real-time.