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In practical speaker identification applications, the performance of systems is generally degraded because of the presence of background noise. In this paper, an advanced hybrid model VQ/GMM-UBM in speaker identification which is a combination of Vector Quantization (VQ) and Gaussian Mixture Model-Universal Background Model (GMM-UBM) is presented. Even though this algorithm takes advantages of both...
Power consumption in programmable devices has become a primary factor in design flow. Among the main concerns of power consumption, application performance, battery life, thermal challenges, or reliability, power consumption is crucial in FPGA designs for powered battery equipment. In this paper, we study the FPGA-based design for Sobel Edge Detection algorithm for low cost fall detector and we present...
Nowadays, there are many fall detection systems based on intelligent video analysis. However, these systems are still facing many challenges such as lighting changes, long-term scene changes or added static background objects in new scene, etc. In this paper, adaptive background Gaussian mixture model (GMM) has been applied for moving object segmentation. An ellipse shape has been built from the segmented...
In this paper, a fall detection algorithm has been built using intelligent analysis of captured video signal. Five geometrical features are extracted from input video signal and are recognized by a trained feed-forward neural network. Experimental results on our self-built database show that the proposed fall detection system can detect fall events with quite high precision under different falling...
The goal of this paper is to evaluate the wavelet/frequency-based voice activity detection (VAD) algorithms under harsh conditions. A new frequency-based speech classifier has been developed based on a single subband distance feature in cooperating with adaptive percentile filter. Experimental results in clean, noisy and reverberant environments are provided. Results show that: (i) the group of algorithms...
In this study, we develop two new algorithms based on generalized cross-correlation (GCC) approach for improving multisource detection-localization performance. The algorithms are based on time-delay estimation using classical cross-correlation (CC) and smoothed coherence transform (SCOT) methods. Beside assessment of the two proposed GCC methods with the existing GCC-PHAT method, peformance of the...
In this paper, we analyze the performance of wavelet-based voice activity detection (VAD) algorithms with respect to the detection of target speech. In addition, the state-of-the-art VAD standardized for the G. 729 B, the ETSI AFE ES 202 050 are evaluated extensively. Experimental results on a self-built cocktail party corpus including different target-interference speech activity conditions are provided...
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