Content-Based Classification, Search, and Retrieval of Audio
IEEE MultiMedia
Fast Recognition of Musical Genres Using RBF Networks
IEEE Transactions on Knowledge and Data Engineering
Real-time discrimination of broadcast speech/music
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
An enhanced fuzzy c-means algorithm for audio segmentation and classification
Multimedia Tools and Applications
An analysis of content-based classification of audio signals using a fuzzy c-means algorithm
Multimedia Tools and Applications
| Hi-index | 0.10 |
A content-based audio retrieval method based Fuzzy c-Means algorithm with divergence kernel (FCM-DK) is proposed in this paper. FCM-DK is based on the Fuzzy c-Means algorithm and employs a kernel method for data transformation. The kernel method adopted in FCM-DK is used to transform the feature data of audio signals into a feature space of a higher dimensionality so that nonlinear problems residing in the input space can be linearly solved in the feature space. In order to deal with Gaussian probability density function (GPDF) data, a divergence-based kernel employing a divergence distance measure for its similarity measure is used for data transformation. The proposed method exploits the statistical nature of the audio data to improve the classification accuracy. Experiments and results on various data sets demonstrate that the proposed classification method outperforms conventional algorithms such as the traditional self-organizing map (SOM) and the Fuzzy c-Means (FCM) 20.83% and 17.5% in terms of accuracy.