Estimation of multiple source component using genetic algorithm
نویسندگان
چکیده
—Source of speech signal consists of voiced part and unvoiced part. In conventional source-filter model, those two sources are considered to be independent. But in real situation it is difficult to segregate the source into voiced and unvoiced part. Actual source consists of mixture of two sources and the ratio varies according to the contents or intention of the speaker. In this paper we tried to segregate the components of voiced and unvoiced while considering source models. Source signals are modeled based on residual signal measured from inverse filtering. Two kinds of source models are assumed. Each model parameters is optimized to the original speech signal using genetic algorithm. The resulting parameters were compared in terms of the mel-cepstral distance to the original signal, spectrogram and spectral envelope from the synthesized signal.
منابع مشابه
An optimization technique for vendor selection with quantity discounts using Genetic Algorithm
Vendor selection decisions are complicated by the fact that various conflicting multi-objective factors must be considered in the decision making process. The problem of vendor selection becomes still more compli-cated with the inclusion of incremental discount pricing schedule. Such hard combinatorial problems when solved using meta heuristics produce near optimal solutions. This paper propose...
متن کاملThreshold F-policy and N-policy for multi-component machining system with warm standbys
The integration of marketing and demand with logistics and inventories (supply side of companies) may cause multiple improvements; it can revolutionize the management of the revenue of rental companies, hotels, and airlines. In this paper, we develop a multi-objective pricing-inventory model for a retailer. Maximizing the retailer's profit and the service level are the objectives, and shorta...
متن کاملResearch of Blind Signals Separation with Genetic Algorithm and Particle Swarm Optimization Based on Mutual Information
Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...
متن کاملResearch of Blind Signals Separation with Genetic Algorithm and Particle Swarm Optimization Based on Mutual Information
Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...
متن کاملForecasting and Sensitivity Analysis of Monthly Evaporation from Siah Bisheh Dam Reservoir using Artificial neural Networks combined with Genetic Algorithm
Evaporation process, the main component of the water cycle in nature, is essential in agricultural studies, hydrology and meteorology, the operation of reservoirs, irrigation and drainage systems, irrigation scheduling and management of water resources. Various methods have been presented for estimating evaporation from free surface including water budget method, evaporation from pan and experi...
متن کاملEstimation of groundwater level using a hybrid genetic algorithm-neural network
In this paper, we present an application of evolved neural networks using a real coded genetic algorithm for simulations of monthly groundwater levels in a coastal aquifer located in the Shabestar Plain, Iran. After initializing the model with groundwater elevations observed at a given time, the developed hybrid genetic algorithm-back propagation (GA-BP) should be able to reproduce groundwater ...
متن کامل