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An enhanced fuzzy c-means algorithm for audio segmentation and classification

MULTIMEDIA TOOLS AND APPLICATIONS 2013年第2期63卷 485-500页

作者： Haque, Mohammad A. Kim, Jong-Myon Univ Ulsan Sch Elect Engn Ulsan 689749 South Korea

Automated audio segmentation and classification play important roles in multimedia content analysis. In this paper, we propose an enhanced approach, called the correlation intensive fuzzy c-means (CIFCM) algorithm, to audio segmentation and classification that is based on audio content analysis. While conventional methods work by considering the attributes of only the current frame or segment, the proposed CIFCM algorithm efficiently incorporates the influence of neighboring frames or segments in the audio stream. With this method, audio-cuts can be detected efficiently even when the signal contains audio effects such as fade-in, fade-out, and cross-fade. A number of audio features are analyzed in this paper to explore the differences between various types of audio data. The proposed CIFCM algorithm works by detecting the boundaries between different kinds of sounds and classifying them into clusters such as silence, speech, music, speech with music, and speech with noise. Our experimental results indicate that the proposed method outperforms the state-of-the-art FCM approach in terms of audio segmentation and classification.

关键词： audio segmentation and classification Fuzzy c-means algorithm Multimedia Database retrieval

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SPORTS audio segmentation and classification

SPORTS AUDIO SEGMENTATION AND CLASSIFICATION

引用

IEEE International Conference on Network Infrastructure and Digital Content

作者： Huang, Jun Dong, Yuan Liu, Jiqing Dong, Chengyu Wang, Haila Beijing Univ Posts & Telecommun Beijing 100088 Peoples R China France Telecom Res & Dev Ctr Beijing Peoples R China

ISBN: (纸本)9781424448982

The audio stream is an important component of a sports video. In this paper, we present a system for audio segmentation and classification, which can segment and classify the sports audio stream into speech, non-speech very well. The novel point in our research is that we apply the segmentation and clustering method which is often used in speaker diarization system for broadcast news to the analysis of sports videos. After the segmentation and Bayesian information Criterion (BIC) clustering is performed, Gaussian Mixture Model (GMM) is used in the classifier to identify the kind of sound for each segment. Experiments on a database composed of 6 hour audio stream in the Eurosport TV program show that the average accuracy can reach 87.3% on segmentation and classification. This research is very useful for analyzing the content of sports videos in detail.

关键词： audio segmentation and classification sports audio GMM content analysis

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SPORTS audio segmentation and classification

SPORTS AUDIO SEGMENTATION AND CLASSIFICATION

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2009 IEEE International Conference on Network Infrastructure and Digital Content（IC-NIDC 2009）

作者： Jun Huang 1,Yuan Dong 1,Jiqing Liu 1,Chengyu Dong 2,Haila Wang 2 1Beijing University of Posts and Telecommunications,Beijing 2France Telecom Research & Development Center,Beijing

The audio stream is an important component of a sports *** this paper,we present a system for audio segmentation and classification,which can segment and classify the sports audio stream into speech,non-speech very *** novel point in our research is that we apply the segmentation and clustering method which is often used in speaker diarization system for broadcast news to the analysis of sports *** the segmentation and Bayesian Information Criterion (BIC) clustering is performed,Gaussian Mixture Model (GMM) is used in the classifier to identify the kind of sound for each *** on a database composed of 6 hour audio stream in the Eurosport TV program show that the average accuracy can reach 87.3% on segmentation and *** research is very useful for analyzing the content of sports videos in detail.

关键词： audio segmentation and classification sports audio GMM content analysis

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SPORTS audio segmentation and classification

SPORTS AUDIO SEGMENTATION AND CLASSIFICATION

引用

2009 IEEE International Conference on Network Infrastructure and Digital Content(2009年IEEE网络基础设施与数字内容国际会议 IEEE IC-NIDC2009)

作者： Jiqing Liu Yuan Dong Jun Huang Haila Wang Chengyu Dong Beijing University of Posts and Telecommunications Beijing France Telecom Research & Development Center Beijing

The audio stream is an important component of a sports video. In this paper, we present a system for audio segmentation and classification, which can segment and classify the sports audio stream into speech, non-speech very well. The novel point in our research is that we apply the segmentation and clustering method which is often used in speaker diarization system for broadcast news to the analysis of sports videos. After the segmentation and Bayesian Information Criterion (BIC) clustering is performed, Gaussian Mixture Model (GMM) is used in the classifier to identify the kind of sound for each segment. Experiments on a database composed of 6 hour audio stream in the Eurosport TV program show that the average accuracy can reach 87.3% on segmentation and classification. This research is very useful for analyzing the content of sports videos in detail.

关键词： audio segmentation and classification sports audio GMM content analysis

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An analysis of content-based classification of audio signals using a fuzzy c-means algorithm

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2013年第1期63卷 77-92页

作者： Haque, Mohammad A. Kim, Jong-Myon Univ Ulsan Sch Elect Engn Ulsan 680749 South Korea

Content-based audio signal classification into broad categories such as speech, music, or speech with noise is the first step before any further processing such as speech recognition, content-based indexing, or surveillance systems. In this paper, we propose an efficient content-based audio classification approach to classify audio signals into broad genres using a fuzzy c-means (FCM) algorithm. We analyze different characteristic features of audio signals in time, frequency, and coefficient domains and select the optimal feature vector by employing a noble analytical scoring method to each feature. We utilize an FCM-based classification scheme and apply it on the extracted normalized optimal feature vector to achieve an efficient classification result. Experimental results demonstrate that the proposed approach outperforms the existing state-of-the-art audio classification systems by more than 11% in classification performance.

关键词： audio segmentation and classification Fuzzy c-means algorithm Multimedia Database retrieval

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Towards robust features for classifying audio in the CueVideo system 99

Towards robust features for classifying audio in the CueVide...

引用

7th ACM International Multimedia Conference

作者： Srinivasan, S Petkovic, D Ponceleon, D IBM Corp Almaden Res Ctr San Jose CA 95120 USA

ISBN: (纸本)0201485621

The role of audio in the context of multimedia applications involving video is becoming increasingly important. Many efforts in this area focus on audio data that contains some built-in semantic information structure such as in broadcast news, or focus on classification of audio that contains a single type of sound such as clear speech or clear music only. In the CueVideo system, we detect and classify audio that consists of mixed audio, i.e. combinations of speech and music together with other types of background sounds. segmentation of mixed audio has applications in detection of story boundaries in video, spoken document retrieval systems, audio retrieval systems etc. We modify and combine audio features known to be effective in distinguishing speech from music, and examine their behavior on mixed audio. Our preliminary experimental results show that we can achieve a classification accuracy of over 80% for such mixed audio. Our study also provides us with several helpful insights related to analyzing mixed audio in the context of real "events" like silence, music, explosions, applause etc. Often applications.

关键词： audio segmentation and classification speech/music discrimination

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Content-based classification and retrieval of audio

Content-based classification and retrieval of audio

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SPIE Conference on Advanced Signal Processing Algorithms, Architectures, and Implementations VIII

作者： Zhang, T Kuo, CCJ Univ So Calif Integrated Media Syst Ctr Los Angeles CA 90089 USA

ISBN: (纸本)0819429163

An online audio classification and segmentation system is presented in this research, where audio recordings are classified and segmented into speech, music, several types of environmental sounds and silence based on audio content analysis. This is the first step of our continuing work towards a general content-based audio classification and retrieval system. The extracted audio features include temporal curves of the energy function, the average zero-crossing rate, the fundamental frequency of audio signals, as well as statistical and morphological features of these curves. The classification result is achieved through a threshold-based heuristic procedure. The audio database that we have built, details of feature extraction, classification and segmentation procedures, and experimental results are described. It is shown that, with the proposed new system, audio recordings can be automatically segmented and classified into basic types in real time with an accuracy of over 90%. Outlines of further classification of audio into finer types and a query-by-example audio retrieval system on top of the coarse classification are also introduced.

关键词： audio content analysis audio segmentation and classification audio database average zero-crossing rate fundamental frequency

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