A schema for content-based analysis of broadcast news video is presented. First, we separate commercials from news using audiovisual features. Then, we automatically organize news programs into a content hierarchy at ...A schema for content-based analysis of broadcast news video is presented. First, we separate commercials from news using audiovisual features. Then, we automatically organize news programs into a content hierarchy at various levels of abstraction via effective integration of video, audio, and text data available from the news programs. Based on these news video structure and content analysis technologies, a TV news video Library is generated, from which users can retrieve definite news story according to their demands.展开更多
Abstract： A hierarchical method for scene analysis in audio sensor networks is proposed. This meth-od consists of two stages： element detection stage and audio scene analysis stage. In the former stage, the basic au...Abstract： A hierarchical method for scene analysis in audio sensor networks is proposed. This meth-od consists of two stages： element detection stage and audio scene analysis stage. In the former stage, the basic audio elements are modeled by the HMM models and trained by enough samples off-line, and we adaptively add or remove basic ele- ment from the targeted element pool according to the time, place and other environment parameters. In the latter stage, a data fusion algorithm is used to combine the sensory information of the same ar-ea, and then, a role-based method is employed to analyze the audio scene based on the fused data. We conduct some experiments to evaluate the per-formance of the proposed method that about 70% audio scenes can be detected correctly by this method. The experiment evaluations demonstrate that our method can achieve satisfactory results.展开更多
Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstruc...Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.展开更多
Recent years, problems of the current college English courses, such as test-oriented teaching, wasteful duplication of language skills teaching, etc., have been criticized by educators and students in China. Thus, tur...Recent years, problems of the current college English courses, such as test-oriented teaching, wasteful duplication of language skills teaching, etc., have been criticized by educators and students in China. Thus, turning skill-based English courses into content-based ESP （English for Special Purposes） courses has become a tendency at colleges and universities in China. The purpose of this paper is to analyze factors influencing the design of such courses through the means of questionnaire, individual interviews, data survey （e.g., educational plans） etc.. We found the following factors throw light on the questions relating to curriculum design of ESP courses： （1） the learners＇ learning needs; （2） the social needs （esp. labor market needs）; and （3） the academic needs from the university. The analysis serves to reveal the gap between the school system and actual social and students needs. With these factors in mind, designers of such courses can adjust goals, contents, approaches, and assessments in the practical teaching. And thus these factors would enable content-based ESP courses to reflect learners＇ needs better and lead to more effective langrage learning in college English teaching field.展开更多
With the development of cloud-based data centers and multimedia technologies, cloud-based multimedia service systems have been paid more and more attention. Audio highlights detection plays an important role in the cl...With the development of cloud-based data centers and multimedia technologies, cloud-based multimedia service systems have been paid more and more attention. Audio highlights detection plays an important role in the cloud-based multimedia service system. In this paper, we proposed a novel highlight detection method to extract the audio highlight effects for the cloud-based multimedia service system using the unsupervised approach. In the proposed method, we first extract the audio features for each audio document. Then the spectral clustering scheme was used to decompose the audio document into several audio effects. Then, we introduce the TF-IDF method to label the highlight effect. We design some experiments to evaluate the performance of the proposed method, and the experimental results show that our method can achieve satisfying results.展开更多
Video data are composed of multimodal information streams including visual, auditory and textual streams, so an approach of story segmentation for news video using multimodal analysis is described in this paper. The p...Video data are composed of multimodal information streams including visual, auditory and textual streams, so an approach of story segmentation for news video using multimodal analysis is described in this paper. The proposed approach detects the topic-caption frames, and integrates them with silence clips detection results, as well as shot segmentation results to locate the news story boundaries. The integration of audio-visual features and text information overcomes the weakness of the approach using only image analysis techniques. On test data with 135 400 frames, when the boundaries between news stories are detected, the accuracy rate 85.8% and the recall rate 97.5% are obtained. The experimental results show the approach is valid and robust.展开更多
基金Supported by the Science Item of National Power Company( No.SPKJ0 16 -0 71)
文摘A schema for content-based analysis of broadcast news video is presented. First, we separate commercials from news using audiovisual features. Then, we automatically organize news programs into a content hierarchy at various levels of abstraction via effective integration of video, audio, and text data available from the news programs. Based on these news video structure and content analysis technologies, a TV news video Library is generated, from which users can retrieve definite news story according to their demands.
基金This work was supported by the Projects of the National Nat-ura! Science Foundation of China under Crant No.U0835001 the Fundamental Research Funds for the Central Universities-2011PTB-00-28.
文摘Abstract： A hierarchical method for scene analysis in audio sensor networks is proposed. This meth-od consists of two stages： element detection stage and audio scene analysis stage. In the former stage, the basic audio elements are modeled by the HMM models and trained by enough samples off-line, and we adaptively add or remove basic ele- ment from the targeted element pool according to the time, place and other environment parameters. In the latter stage, a data fusion algorithm is used to combine the sensory information of the same ar-ea, and then, a role-based method is employed to analyze the audio scene based on the fused data. We conduct some experiments to evaluate the per-formance of the proposed method that about 70% audio scenes can be detected correctly by this method. The experiment evaluations demonstrate that our method can achieve satisfactory results.
基金funded by the Directorate of Research and Community Service,Directorate General of Research and Development,Ministry of Higher Education,Science and Technologyin accordance with the Implementation Contract for the Operational Assistance Program for State Universities,Research Program Number:109/C3/DT.05.00/PL/2025.
文摘Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.
文摘Recent years, problems of the current college English courses, such as test-oriented teaching, wasteful duplication of language skills teaching, etc., have been criticized by educators and students in China. Thus, turning skill-based English courses into content-based ESP （English for Special Purposes） courses has become a tendency at colleges and universities in China. The purpose of this paper is to analyze factors influencing the design of such courses through the means of questionnaire, individual interviews, data survey （e.g., educational plans） etc.. We found the following factors throw light on the questions relating to curriculum design of ESP courses： （1） the learners＇ learning needs; （2） the social needs （esp. labor market needs）; and （3） the academic needs from the university. The analysis serves to reveal the gap between the school system and actual social and students needs. With these factors in mind, designers of such courses can adjust goals, contents, approaches, and assessments in the practical teaching. And thus these factors would enable content-based ESP courses to reflect learners＇ needs better and lead to more effective langrage learning in college English teaching field.
基金supported by National Development and Reform Commission Information Security Special FundNational Key Basic Reseerch Program of China (973 program) under Grant No.2007CB311203
文摘With the development of cloud-based data centers and multimedia technologies, cloud-based multimedia service systems have been paid more and more attention. Audio highlights detection plays an important role in the cloud-based multimedia service system. In this paper, we proposed a novel highlight detection method to extract the audio highlight effects for the cloud-based multimedia service system using the unsupervised approach. In the proposed method, we first extract the audio features for each audio document. Then the spectral clustering scheme was used to decompose the audio document into several audio effects. Then, we introduce the TF-IDF method to label the highlight effect. We design some experiments to evaluate the performance of the proposed method, and the experimental results show that our method can achieve satisfying results.
文摘Video data are composed of multimodal information streams including visual, auditory and textual streams, so an approach of story segmentation for news video using multimodal analysis is described in this paper. The proposed approach detects the topic-caption frames, and integrates them with silence clips detection results, as well as shot segmentation results to locate the news story boundaries. The integration of audio-visual features and text information overcomes the weakness of the approach using only image analysis techniques. On test data with 135 400 frames, when the boundaries between news stories are detected, the accuracy rate 85.8% and the recall rate 97.5% are obtained. The experimental results show the approach is valid and robust.
