Speech separation is an active research topic that plays an important role in numerous applications,such as speaker recognition,hearing pros-thesis,and autonomous robots.Many algorithms have been put forward to improv...Speech separation is an active research topic that plays an important role in numerous applications,such as speaker recognition,hearing pros-thesis,and autonomous robots.Many algorithms have been put forward to improve separation performance.However,speech separation in reverberant noisy environment is still a challenging task.To address this,a novel speech separation algorithm using gate recurrent unit(GRU)network based on microphone array has been proposed in this paper.The main aim of the proposed algorithm is to improve the separation performance and reduce the computational cost.The proposed algorithm extracts the sub-band steered response power-phase transform(SRP-PHAT)weighted by gammatone filter as the speech separation feature due to its discriminative and robust spatial position in formation.Since the GRU net work has the advantage of processing time series data with faster training speed and fewer training parameters,the GRU model is adopted to process the separation featuresof several sequential frames in the same sub-band to estimate the ideal Ratio Masking(IRM).The proposed algorithm decomposes the mixture signals into time-frequency(TF)units using gammatone filter bank in the frequency domain,and the target speech is reconstructed in the frequency domain by masking the mixture signal according to the estimated IRM.The operations of decomposing the mixture signal and reconstructing the target signal are completed in the frequency domain which can reduce the total computational cost.Experimental results demonstrate that the proposed algorithm realizes omnidirectional speech sep-aration in noisy and reverberant environments,provides good performance in terms of speech quality and intelligibility,and has the generalization capacity to reverberate.展开更多
The general problem of this research was how students respond to hate speech.The purpose of the study was to obtain an overview of(1)perceptions;(2)attitudes;and(3)student actions/participation towards hate speech.The...The general problem of this research was how students respond to hate speech.The purpose of the study was to obtain an overview of(1)perceptions;(2)attitudes;and(3)student actions/participation towards hate speech.The research approach used was quantitative and descriptive with survey method.The population of this study was all the administrators of the student executive board in UNTAN,IAIN,and IKIP PGRI Pontianak totaling 162 students.The number of research samples was 115 students determined by Slovin formula.The respondents were choosen randomly.Data collection used a questionnaire.Data analysis used percentage quantitative descriptive analysis techniques.The general conclusion of the study shows that student responses to hate speech are good.Specific conclusions of the study are:(1)student perceptions(knowledge)of hate speech are on average 78.26%know and 21.74%do not know about the utterances of hatred;(2)student attitudes towards hate speech are on average 78.14%students do not agree with hate speech and 21.86%agree;and(3)student actions or participation in hate speech are on average 78.51%students never take acts in hate speech and 21.49%ever.展开更多
基金This work is supported by Nanjing Institute of Technology(NIT)fund for Research Startup Projects of Introduced talents under Grant No.YKJ202019Nature Sci-ence Research Project of Higher Education Institutions in Jiangsu Province under Grant No.21KJB510018+1 种基金National Nature Science Foundation of China(NSFC)under Grant No.62001215NIT fund for Doctoral Research Projects under Grant No.ZKJ2020003.
文摘Speech separation is an active research topic that plays an important role in numerous applications,such as speaker recognition,hearing pros-thesis,and autonomous robots.Many algorithms have been put forward to improve separation performance.However,speech separation in reverberant noisy environment is still a challenging task.To address this,a novel speech separation algorithm using gate recurrent unit(GRU)network based on microphone array has been proposed in this paper.The main aim of the proposed algorithm is to improve the separation performance and reduce the computational cost.The proposed algorithm extracts the sub-band steered response power-phase transform(SRP-PHAT)weighted by gammatone filter as the speech separation feature due to its discriminative and robust spatial position in formation.Since the GRU net work has the advantage of processing time series data with faster training speed and fewer training parameters,the GRU model is adopted to process the separation featuresof several sequential frames in the same sub-band to estimate the ideal Ratio Masking(IRM).The proposed algorithm decomposes the mixture signals into time-frequency(TF)units using gammatone filter bank in the frequency domain,and the target speech is reconstructed in the frequency domain by masking the mixture signal according to the estimated IRM.The operations of decomposing the mixture signal and reconstructing the target signal are completed in the frequency domain which can reduce the total computational cost.Experimental results demonstrate that the proposed algorithm realizes omnidirectional speech sep-aration in noisy and reverberant environments,provides good performance in terms of speech quality and intelligibility,and has the generalization capacity to reverberate.
文摘The general problem of this research was how students respond to hate speech.The purpose of the study was to obtain an overview of(1)perceptions;(2)attitudes;and(3)student actions/participation towards hate speech.The research approach used was quantitative and descriptive with survey method.The population of this study was all the administrators of the student executive board in UNTAN,IAIN,and IKIP PGRI Pontianak totaling 162 students.The number of research samples was 115 students determined by Slovin formula.The respondents were choosen randomly.Data collection used a questionnaire.Data analysis used percentage quantitative descriptive analysis techniques.The general conclusion of the study shows that student responses to hate speech are good.Specific conclusions of the study are:(1)student perceptions(knowledge)of hate speech are on average 78.26%know and 21.74%do not know about the utterances of hatred;(2)student attitudes towards hate speech are on average 78.14%students do not agree with hate speech and 21.86%agree;and(3)student actions or participation in hate speech are on average 78.51%students never take acts in hate speech and 21.49%ever.
