• Title, Summary, Keyword: content features

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Music Genre Classification Based on Timbral Texture and Rhythmic Content Features

  • Baniya, Babu Kaji;Ghimire, Deepak;Lee, Joonwhon
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.204-207
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    • 2013
  • Music genre classification is an essential component for music information retrieval system. There are two important components to be considered for better genre classification, which are audio feature extraction and classifier. This paper incorporates two different kinds of features for genre classification, timbral texture and rhythmic content features. Timbral texture contains several spectral and Mel-frequency Cepstral Coefficient (MFCC) features. Before choosing a timbral feature we explore which feature contributes less significant role on genre discrimination. This facilitates the reduction of feature dimension. For the timbral features up to the 4-th order central moments and the covariance components of mutual features are considered to improve the overall classification result. For the rhythmic content the features extracted from beat histogram are selected. In the paper Extreme Learning Machine (ELM) with bagging is used as classifier for classifying the genres. Based on the proposed feature sets and classifier, experiment is performed with well-known datasets: GTZAN databases with ten different music genres, respectively. The proposed method acquires the better classification accuracy than the existing approaches.

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Harmonic Structure Features for Robust Speaker Diarization

  • Zhou, Yu;Suo, Hongbin;Li, Junfeng;Yan, Yonghong
    • ETRI Journal
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    • v.34 no.4
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    • pp.583-590
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    • 2012
  • In this paper, we present a new approach for speaker diarization. First, we use the prosodic information calculated on the original speech to resynthesize the new speech data utilizing the spectrum modeling technique. The resynthesized data is modeled with sinusoids based on pitch, vibration amplitude, and phase bias. Then, we use the resynthesized speech data to extract cepstral features and integrate them with the cepstral features from original speech for speaker diarization. At last, we show how the two streams of cepstral features can be combined to improve the robustness of speaker diarization. Experiments carried out on the standardized datasets (the US National Institute of Standards and Technology Rich Transcription 04-S multiple distant microphone conditions) show a significant improvement in diarization error rate compared to the system based on only the feature stream from original speech.

Intra-and Inter-frame Features for Automatic Speech Recognition

  • Lee, Sung Joo;Kang, Byung Ok;Chung, Hoon;Lee, Yunkeun
    • ETRI Journal
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    • v.36 no.3
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    • pp.514-517
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    • 2014
  • In this paper, alternative dynamic features for speech recognition are proposed. The goal of this work is to improve speech recognition accuracy by deriving the representation of distinctive dynamic characteristics from a speech spectrum. This work was inspired by two temporal dynamics of a speech signal. One is the highly non-stationary nature of speech, and the other is the inter-frame change of a speech spectrum. We adopt the use of a sub-frame spectrum analyzer to capture very rapid spectral changes within a speech analysis frame. In addition, we attempt to measure spectral fluctuations of a more complex manner as opposed to traditional dynamic features such as delta or double-delta. To evaluate the proposed features, speech recognition tests over smartphone environments were conducted. The experimental results show that the feature streams simply combined with the proposed features are effective for an improvement in the recognition accuracy of a hidden Markov model-based speech recognizer.

Content Based Image Retrieval Using Combined Features of Shape, Color and Relevance Feedback

  • Mussarat, Yasmin;Muhammad, Sharif;Sajjad, Mohsin;Isma, Irum
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.7 no.12
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    • pp.3149-3165
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    • 2013
  • Content based image retrieval is increasingly gaining popularity among image repository systems as images are a big source of digital communication and information sharing. Identification of image content is done through feature extraction which is the key operation for a successful content based image retrieval system. In this paper content based image retrieval system has been developed by adopting a strategy of combining multiple features of shape, color and relevance feedback. Shape is served as a primary operation to identify images whereas color and relevance feedback have been used as supporting features to make the system more efficient and accurate. Shape features are estimated through second derivative, least square polynomial and shapes coding methods. Color is estimated through max-min mean of neighborhood intensities. A new technique has been introduced for relevance feedback without bothering the user.

Steganalysis of Content-Adaptive Steganography using Markov Features for DCT Coefficients (DCT 계수의 마코프 특징을 이용한 내용 적응적 스테가노그래피의 스테그분석)

  • Park, Tae Hee;Han, Jong Goo;Eom, Il Kyu
    • Journal of the Institute of Electronics and Information Engineers
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    • v.52 no.8
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    • pp.97-105
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    • 2015
  • Content-adaptive steganography methods embed secret messages in hard-to-model regions of covers such as complicated texture or noisy area. Content-adaptive steganalysis methods often need high dimensional features to capture more subtle relationships of local dependencies among adjacent pixels. However, these methods require many computational complexity and depend on the location of hidden message and the exploited distortion metrics. In this paper, we propose an improved steganalysis method for content-adaptive steganography to enhance detection rate with small number features. We first show that the features form the difference between DCT coefficients are useful for analyzing the content-adaptive steganography methods, and present feature extraction mehtod using first-order Markov probability for the the difference between DCT coefficients. The extracted features are used as input of ensemble classifier. Experimental results show that the proposed method outperforms previous schemes in terms of detection rates and accuracy in spite of a small number features in various content-adaptive stego images.

Image Clustering using Color, Texture and Shape Features

  • Sleit, Azzam;Abu Dalhoum, Abdel Llatif;Qatawneh, Mohammad;Al-Sharief, Maryam;Al-Jabaly, Rawa'a;Karajeh, Ola
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.5 no.1
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    • pp.211-227
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    • 2011
  • Content Based Image Retrieval (CBIR) is an approach for retrieving similar images from an image database based on automatically-derived image features. The quality of a retrieval system depends on the features used to describe image content. In this paper, we propose an image clustering system that takes a database of images as input and clusters them using k-means clustering algorithm taking into consideration color, texture and shape features. Experimental results show that the combination of the three features brings about higher values of accuracy and precision.

Content-Based Image Retrieval Using Multi-Resolution Multi-Direction Filtering-Based CLBP Texture Features and Color Autocorrelogram Features

  • Bu, Hee-Hyung;Kim, Nam-Chul;Yun, Byoung-Ju;Kim, Sung-Ho
    • Journal of Information Processing Systems
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    • v.16 no.4
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    • pp.991-1000
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    • 2020
  • We propose a content-based image retrieval system that uses a combination of completed local binary pattern (CLBP) and color autocorrelogram. CLBP features are extracted on a multi-resolution multi-direction filtered domain of value component. Color autocorrelogram features are extracted in two dimensions of hue and saturation components. Experiment results revealed that the proposed method yields a lot of improvement when compared with the methods that use partial features employed in the proposed method. It is also superior to the conventional CLBP, the color autocorrelogram using R, G, and B components, and the multichannel decoded local binary pattern which is one of the latest methods.

The Effects of YouTube Summary Contents Features and Contents Provider Credibility on Users' Flow and Satisfaction (유튜브 서머리 콘텐츠 특성과 콘텐츠 제공자 신뢰성이 이용자 몰입과 만족에 미치는 영향)

  • Jeong, Yu-Jin;Lee, Nam-Jung;Lee, Jung-Hoon
    • Journal of the Korea Convergence Society
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    • v.12 no.2
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    • pp.35-44
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    • 2021
  • Previous studies have studied short videos, short form content, snack culture and so on, but few studies have been conducted on the form of summary content that compressing and summarizing the original content. This study aims to contribute to the revitalization of the summary content market by exploring ways to enhance user satisfaction through analysis of the YouTube summary content features and the credibility of content providers that bring about flow and satisfaction of YouTube summary content users. The survey was conducted on 202 people who have watched YouTube summary contents for finding out the effects of YouTube summary contents features and content provider credibility on the details of flow. As a result, only entertainment had a significant impact on all flow details. This study is of academic significance in that it defines the features of YouTube summary contents, and has practical significance in that it suggests what direction the summary content should have in order to arouse user satisfaction in future.

Research of Adaptive Transformation Method Based on Webpage Semantic Features for Small-Screen Terminals

  • Li, Hao;Liu, Qingtang;Hu, Min;Zhu, Xiaoliang
    • ETRI Journal
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    • v.35 no.5
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    • pp.900-910
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    • 2013
  • Small-screen mobile terminals have difficulty accessing existing Web resources designed for large-screen devices. This paper presents an adaptive transformation method based on webpage semantic features to solve this problem. According to the text density and link density features of the webpages, the webpages are divided into two types: index and content. Our method uses an index-based webpage transformation algorithm and a content-based webpage transformation algorithm. Experiment results demonstrate that our adaptive transformation method is not dependent on specific software and webpage templates, and it is capable of enhancing Web content adaptation on small-screen terminals.

A Study on Extraction and Comparison of Digital Content Key Frame in UCC Service Environment

  • Jang, Eun-Gyeom
    • Journal of Korea Multimedia Society
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    • v.14 no.8
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    • pp.1020-1028
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    • 2011
  • In this paper, we proposed a mechanism that prevents indiscreet use of digital contents, verifies created content's copyrights to provide services to granted user and protects digital contents by law by authenticating the original digital content whenever an infringement of copyright occurs in UCC environments. The proposed mechanism uses specific information and features of contents as copyrights authentication information without additional information. Also, provides the fact of violation by inferring the modification of the original digital contents. That means this mechanism infers same or similar value from the contents; fraudulent use of content, modification of content color, modification of content format, modification of content resolution and illegal use of frame not principal key frame. In that point, we found that the inferred value from the content differed according to features of content modification.