Accidental Wow Defect Evaluation Using Sinusoidal Analysis Enhanced by Artificial Neural Networks
نویسندگان
چکیده
A method for evaluation of parasitic frequency modulation (wow) in archival audio is presented. The proposed approach utilizes sinusoidal components as their variations are highly correlated with the distortion variations. The sinusoidal components are extracted from audio signal by means of sinusoidal modeling procedures being often severely distorted and in case of wow also significantly modulated. The algorithm for sinusoidal component evaluation utilizes both magnitude and phase spectra information to enhance the tracking process. Additionally, a neural-network based prediction module is proposed to improve the tracking abilities in case of component discontinuities. Experiments concerning prediction of tonal component’s values are performed revealing that prediction can enhance sinusoidal modeling of wow distorted signals effectively.
منابع مشابه
Accidental Wow Evaluation Based on Sinusoidal Modeling and Neural Nets Prediction
In this paper an algorithmic approach to the wow defect characteristic evaluation is presented. The approach is based on a sinusoidal analysis comprising both amplitude and phase spectra processing. The frequency trajectories depicting the distortion are built on a basis of amplitude, frequency and phase dependencies and are further used for wow characteristic evaluation. Additionally the exper...
متن کاملEvaluation of effects of operating parameters on combustible material recovery in coking coal flotation process using artificial neural networks
In this research work, the effects of flotation parameters on coking coal flotation combustible material recovery (CMR) were studied by the artificial neural networks (ANNs) method. The input parameters of the network were the pulp solid weight content, pH, collector dosage, frother dosage, conditioning time, flotation retention time, feed ash content, and rotor rotation speed. In order to sele...
متن کاملEvaluation of Ultimate Torsional Strength of Reinforcement Concrete Beams Using Finite Element Analysis and Artificial Neural Network
Due to lack of theory of elasticity, estimation of ultimate torsional strength of reinforcement concrete beams is a difficult task. Therefore, the finite element methods could be applied for determination of strength of concrete beams. Furthermore, for complicated, highly nonlinear and ambiguous status, artificial neural networks are appropriate tools for prediction of behavior of such states. ...
متن کاملEvaluation of the Effective Electrospinning Parameters Controlling Kefiran Nanofibers Diameter Using Modelling Artificial Neural Networks
Objective(s): This paper investigates the validity of Artificial Neural Networks (ANN) model in the prediction of electrospun kefiran nanofibers diameter using 4 effective parameters involved in electrospinning process. Polymer concentration, applied voltage, flow rate and nozzle to collector distance were used as variable parameters to design various sets of electrospinning ex...
متن کاملEntrepreneurship policy and innovative indicators of industrial companies: Evaluation by MCDM and ANN Methods
The present paper presented a methodology for prioritizing the innovative and entrepreneurial indicators using Multi Criteria Decision Making (MCDM) and Artificial Neural Networks (ANNs), taking into account three individual, organizational and cultural dimensions simultaneously in decision making procedure. This methodology has two main advantages: first, the speed of operation in the accounti...
متن کامل