A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Audio Feature Extraction and Analysis for Scene Segmentation and Classification
Journal of VLSI Signal Processing Systems - special issue on multimedia signal processing
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Content-Based Classification, Search, and Retrieval of Audio
IEEE MultiMedia
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing (The Handbooks of Fuzzy Sets)
Audio signal segmentation and classification using fuzzy c-means clustering
Systems and Computers in Japan
Content-Based Audio Classification Using Support Vector Machines and Independent Component Analysis
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Classification of audio signals using Fuzzy c-Means with divergence-based Kernel
Pattern Recognition Letters
A generalized spatial fuzzy C-means algorithm for medical image segmentation
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
A robust fuzzy local information C-means clustering algorithm
IEEE Transactions on Image Processing
Classification of audio signals using gradient-based fuzzy c-means algorithm with divergence measure
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
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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.