This article proposes a deep neural network(DNN)-based direct-path relative transfer function(DP-RTF)enhancement method for robust direction of arrival(DOA)estimation in noisy and reverberant environments.The DP-RTF r...This article proposes a deep neural network(DNN)-based direct-path relative transfer function(DP-RTF)enhancement method for robust direction of arrival(DOA)estimation in noisy and reverberant environments.The DP-RTF refers to the ratio between the directpath acoustic transfer functions of the two microphone channels.First,the complex-value DP-RTF is decomposed into the inter-channel intensity difference,and sinusoidal functions of the inter-channel phase difference in the time-frequency domain.Then,the decomposed DP-RTF features from a series of temporal context frames are utilized to train a DNN model,which maps the DP-RTF features contaminated by noise and reverberation to the clean ones,and meanwhile provides a time-frequency(TF)weight to indicate the reliability of the mapping.The DP-RTF enhancement network can help to enhance the DP-RTF against noise and reverberation.Finally,the DOA of a sound source can be estimated by integrating the weighted matching between the enhanced DP-RTF features and the DP-RTF templates.Experimental results on simulated data show the superiority of the proposed DP-RTF enhancement network for estimating the DOA of the sound source in the environments with various levels of noise and reverberation.展开更多
Virtual materials screening approaches have proliferated in the past decade,driven by rapid advances in first-principles computational techniques,and machine-learning algorithms.By comparison,computationally driven ma...Virtual materials screening approaches have proliferated in the past decade,driven by rapid advances in first-principles computational techniques,and machine-learning algorithms.By comparison,computationally driven materials synthesis screening is still in its infancy,and is mired by the challenges of data sparsity and data scarcity:Synthesis routes exist in a sparse,highdimensional parameter space that is difficult to optimize over directly,and,for some materials of interest,only scarce volumes of literature-reported syntheses are available.In this article,we present a framework for suggesting quantitative synthesis parameters and potential driving factors for synthesis outcomes.We use a variational autoencoder to compress sparse synthesis representations into a lower dimensional space,which is found to improve the performance of machine-learning tasks.To realize this screening framework even in cases where there are few literature data,we devise a novel data augmentation methodology that incorporates literature synthesis data from related materials systems.We apply this variational autoencoder framework to generate potential SrTiO_(3) synthesis parameter sets,propose driving factors for brookite TiO_(2) formation,and identify correlations between alkali-ion intercalation and MnO_(2) polymorph selection.展开更多
基金supported by National Natural Science Foundation of China(No.61673030,U1613209)Science and Technology Plan Project of Shenzhen(No.JCYJ20200109140410340).
文摘This article proposes a deep neural network(DNN)-based direct-path relative transfer function(DP-RTF)enhancement method for robust direction of arrival(DOA)estimation in noisy and reverberant environments.The DP-RTF refers to the ratio between the directpath acoustic transfer functions of the two microphone channels.First,the complex-value DP-RTF is decomposed into the inter-channel intensity difference,and sinusoidal functions of the inter-channel phase difference in the time-frequency domain.Then,the decomposed DP-RTF features from a series of temporal context frames are utilized to train a DNN model,which maps the DP-RTF features contaminated by noise and reverberation to the clean ones,and meanwhile provides a time-frequency(TF)weight to indicate the reliability of the mapping.The DP-RTF enhancement network can help to enhance the DP-RTF against noise and reverberation.Finally,the DOA of a sound source can be estimated by integrating the weighted matching between the enhanced DP-RTF features and the DP-RTF templates.Experimental results on simulated data show the superiority of the proposed DP-RTF enhancement network for estimating the DOA of the sound source in the environments with various levels of noise and reverberation.
基金funding from the National Science Foundation Award#1534340DMREF that provided support to make this work possible+4 种基金support from the Office of Naval Research(ONR)under Contract No.N00014-16-1-2432the MIT Energy InitiativeNSF CAREER#1553284the Department of Energy’s Basic Energy Science Program through the Materials Project under Grant No.EDCBEEpartially supported by NSERC.
文摘Virtual materials screening approaches have proliferated in the past decade,driven by rapid advances in first-principles computational techniques,and machine-learning algorithms.By comparison,computationally driven materials synthesis screening is still in its infancy,and is mired by the challenges of data sparsity and data scarcity:Synthesis routes exist in a sparse,highdimensional parameter space that is difficult to optimize over directly,and,for some materials of interest,only scarce volumes of literature-reported syntheses are available.In this article,we present a framework for suggesting quantitative synthesis parameters and potential driving factors for synthesis outcomes.We use a variational autoencoder to compress sparse synthesis representations into a lower dimensional space,which is found to improve the performance of machine-learning tasks.To realize this screening framework even in cases where there are few literature data,we devise a novel data augmentation methodology that incorporates literature synthesis data from related materials systems.We apply this variational autoencoder framework to generate potential SrTiO_(3) synthesis parameter sets,propose driving factors for brookite TiO_(2) formation,and identify correlations between alkali-ion intercalation and MnO_(2) polymorph selection.
