An improved speech absence probability estimation was proposed using environmental noise classification for speech enhancement.A relevant noise estimation approach,known as the speech presence uncertainty tracking met...An improved speech absence probability estimation was proposed using environmental noise classification for speech enhancement.A relevant noise estimation approach,known as the speech presence uncertainty tracking method,requires seeking the "a priori" probability of speech absence that is derived by applying microphone input signal and the noise signal based on the estimated value of the "a posteriori" signal-to-noise ratio(SNR).To overcome this problem,first,the optimal values in terms of the perceived speech quality of a variety of noise types are derived.Second,the estimated optimal values are assigned according to the determined noise type which is classified by a real-time noise classification algorithm based on the Gaussian mixture model(GMM).The proposed algorithm estimates the speech absence probability using a noise classification algorithm which is based on GMM to apply the optimal parameter of each noise type,unlike the conventional approach which uses a fixed threshold and smoothing parameter.The performance of the proposed method was evaluated by objective tests,such as the perceptual evaluation of speech quality(PESQ) and composite measure.Performance was then evaluated by a subjective test,namely,mean opinion scores(MOS) under various noise environments.The proposed method show better results than existing methods.展开更多
In this paper,the Fourier spectrum-based strain energy damage detection method for beam-like structures is proposed based on the discrete Fourier transform.The classical strain energy damage detection method localizes...In this paper,the Fourier spectrum-based strain energy damage detection method for beam-like structures is proposed based on the discrete Fourier transform.The classical strain energy damage detection method localizes damage by the comparison of the strain energy between the intact and inspected structures.The evaluation of the 2nd-order derivative term in the strain energy plays a crucial part in the comparison.The classical methods are mostly based on a numerical derivative estimation for this term.The numerical derivative,however,introduces additional disturbances into damage indications.To address this problem,a discrete Fourier transform-based strain energy is proposed with the emphasis of enhancing the performance in noisy condition.The validations conducted on the simulated and experimental data show that the developed method is effective enough for composite beam damage detection in noisy environments.展开更多
基金Project supported by an Inha University Research GrantProject(10031764) supported by the Strategic Technology Development Program of Ministry of Knowledge Economy,Korea
文摘An improved speech absence probability estimation was proposed using environmental noise classification for speech enhancement.A relevant noise estimation approach,known as the speech presence uncertainty tracking method,requires seeking the "a priori" probability of speech absence that is derived by applying microphone input signal and the noise signal based on the estimated value of the "a posteriori" signal-to-noise ratio(SNR).To overcome this problem,first,the optimal values in terms of the perceived speech quality of a variety of noise types are derived.Second,the estimated optimal values are assigned according to the determined noise type which is classified by a real-time noise classification algorithm based on the Gaussian mixture model(GMM).The proposed algorithm estimates the speech absence probability using a noise classification algorithm which is based on GMM to apply the optimal parameter of each noise type,unlike the conventional approach which uses a fixed threshold and smoothing parameter.The performance of the proposed method was evaluated by objective tests,such as the perceptual evaluation of speech quality(PESQ) and composite measure.Performance was then evaluated by a subjective test,namely,mean opinion scores(MOS) under various noise environments.The proposed method show better results than existing methods.
基金supported by the National Natural Science Foundation of China(Grant Nos.51405369&51421004)the National Key Basic Research Program of China(Grant No.2015CB057400)+1 种基金the National Natural Science Foundation of Shaanxi Province(Grant No.2016JQ5049)the Postdoctoral Science Foundation of Shaanxi Province
文摘In this paper,the Fourier spectrum-based strain energy damage detection method for beam-like structures is proposed based on the discrete Fourier transform.The classical strain energy damage detection method localizes damage by the comparison of the strain energy between the intact and inspected structures.The evaluation of the 2nd-order derivative term in the strain energy plays a crucial part in the comparison.The classical methods are mostly based on a numerical derivative estimation for this term.The numerical derivative,however,introduces additional disturbances into damage indications.To address this problem,a discrete Fourier transform-based strain energy is proposed with the emphasis of enhancing the performance in noisy condition.The validations conducted on the simulated and experimental data show that the developed method is effective enough for composite beam damage detection in noisy environments.
