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We present an accurate seizure detection algorithm, and make a detailed comparison of two frequency analysis methods: a widely used stationary method — Fast Fourier Transform (FFT) and a relatively new nonstationary method — Hilbert-Huang Transform (HHT). Two public databases and one our own database were tested. The results show that our algorithm has very high accuracy compared with the state-of-the-art...
Traditional approaches to environmental sounds recognition used acoustic features merely based on time domain or frequency domain. In this paper, a new feature descriptor that uses image texture information is proposed to identify specific environmental sounds based on the recognition of fixed-duration sounds segments where their corresponding spectrums are viewed as gray-level images. The proposed...
This paper presents a new approach for identifying birds automatically from their sounds, which first converts the bird songs into spectrograms and then extracts texture features from this visual time-frequency representation. The approach is inspired by the finding that spectrograms of different birds present distinct textures and can be easily distinguished from one another. In particular, we perform...
Research on various eco-environmental sounds is very important for people to understand a particular area. However, eco-environmental sounds have many specific properties such as the diversity, high background noise and non-stationary structure which make many traditional audio features hard to characterize them accurately. In this paper, a novel feature extraction technique based on Matching Pursuit...
In the field of crop identification with remote sensing technology, current multi-temporal methods usually do not made full use of target crop's temporal features and spectral features. An improved multitemporal masking classification method was proposed for winter wheat identification in Jiaodong Peninsula. The improved method using four temporal MODIS NDVI product images and two temporal TM surface...
In this paper, a novel protein remote homology detection algorithm based on multiple heterogeneous biological features and kernel affinity propagation clustering is proposed. The kernel method is used to integrate the heterogeneous data sources related to protein remote homolog. In addition prediction accuracy method is extended to validate our proposed algorithm.
Many facial image analysis methods rely on learning-based techniques such as Adaboost or SVMs to project classifiers based on the selection of local image filters (e.g., Haar and Gabor filters) from large sets of training data. In general, the learning process consists of selecting discriminative image filters from a large feature pool that contains filters uniformly sampled from the parameter space...
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