In this paper,the influence of ground motion duration on the inelastic displacement ratio,C_(1),of highly damped SDOF systems is studied.For this purpose,two sets of spectrally equivalent long and short duration groun...In this paper,the influence of ground motion duration on the inelastic displacement ratio,C_(1),of highly damped SDOF systems is studied.For this purpose,two sets of spectrally equivalent long and short duration ground motion records were used in an analysis to isolate the effects of ground motion duration on.The effect of duration was evaluated for observed values of C_(1) by considering six ductility levels,and different damping and post-yield stiffness ratios.A new predictive equation of C_(1) also was developed for long and short duration records.Results of non-linear regression analysis of the current study provide an expression with which to quantify the duration effect.Based on the average values of estimated C_(1) ratios for long duration records divided by C_(1) for a short duration set,it is concluded that the maximum difference between long and short duration records occurs when the damping ratio is 0.3 and the post-yield stiffness ratio is equal to zero.展开更多
Speaker separation in complex acoustic environment is one of challenging tasks in speech separation.In practice,speakers are very often unmoving or moving slowly in normal communication.In this case,the spatial featur...Speaker separation in complex acoustic environment is one of challenging tasks in speech separation.In practice,speakers are very often unmoving or moving slowly in normal communication.In this case,the spatial features among the consecutive speech frames become highly correlated such that it is helpful for speaker separation by providing additional spatial information.To fully exploit this information,we design a separation system on Recurrent Neural Network(RNN)with long short-term memory(LSTM)which effectively learns the temporal dynamics of spatial features.In detail,a LSTM-based speaker separation algorithm is proposed to extract the spatial features in each time-frequency(TF)unit and form the corresponding feature vector.Then,we treat speaker separation as a supervised learning problem,where a modified ideal ratio mask(IRM)is defined as the training function during LSTM learning.Simulations show that the proposed system achieves attractive separation performance in noisy and reverberant environments.Specifically,during the untrained acoustic test with limited priors,e.g.,unmatched signal to noise ratio(SNR)and reverberation,the proposed LSTM based algorithm can still outperforms the existing DNN based method in the measures of PESQ and STOI.It indicates our method is more robust in untrained conditions.展开更多
文摘In this paper,the influence of ground motion duration on the inelastic displacement ratio,C_(1),of highly damped SDOF systems is studied.For this purpose,two sets of spectrally equivalent long and short duration ground motion records were used in an analysis to isolate the effects of ground motion duration on.The effect of duration was evaluated for observed values of C_(1) by considering six ductility levels,and different damping and post-yield stiffness ratios.A new predictive equation of C_(1) also was developed for long and short duration records.Results of non-linear regression analysis of the current study provide an expression with which to quantify the duration effect.Based on the average values of estimated C_(1) ratios for long duration records divided by C_(1) for a short duration set,it is concluded that the maximum difference between long and short duration records occurs when the damping ratio is 0.3 and the post-yield stiffness ratio is equal to zero.
基金This work is supported by the National Nature Science Foundation of China(NSFC)under Grant Nos.61571106,61501169,41706103the Fundamental Research Funds for the Central Universities under Grant No.2242013K30010.
文摘Speaker separation in complex acoustic environment is one of challenging tasks in speech separation.In practice,speakers are very often unmoving or moving slowly in normal communication.In this case,the spatial features among the consecutive speech frames become highly correlated such that it is helpful for speaker separation by providing additional spatial information.To fully exploit this information,we design a separation system on Recurrent Neural Network(RNN)with long short-term memory(LSTM)which effectively learns the temporal dynamics of spatial features.In detail,a LSTM-based speaker separation algorithm is proposed to extract the spatial features in each time-frequency(TF)unit and form the corresponding feature vector.Then,we treat speaker separation as a supervised learning problem,where a modified ideal ratio mask(IRM)is defined as the training function during LSTM learning.Simulations show that the proposed system achieves attractive separation performance in noisy and reverberant environments.Specifically,during the untrained acoustic test with limited priors,e.g.,unmatched signal to noise ratio(SNR)and reverberation,the proposed LSTM based algorithm can still outperforms the existing DNN based method in the measures of PESQ and STOI.It indicates our method is more robust in untrained conditions.
