Spectrogram representations of acoustic scenes have achieved competitive performance for acoustic scene classification. Yet, the spectrogram alone does not take into account a substantial amount of time-frequency info...Spectrogram representations of acoustic scenes have achieved competitive performance for acoustic scene classification. Yet, the spectrogram alone does not take into account a substantial amount of time-frequency information. In this study, we present an approach for exploring the benefits of deep scalogram representations, extracted in segments from an audio stream. The approach presented firstly transforms the segmented acoustic scenes into bump and morse scalograms, as well as spectrograms; secondly, the spectrograms or scalograms are sent into pre-trained convolutional neural networks; thirdly,the features extracted from a subsequent fully connected layer are fed into(bidirectional) gated recurrent neural networks, which are followed by a single highway layer and a softmax layer;finally, predictions from these three systems are fused by a margin sampling value strategy. We then evaluate the proposed approach using the acoustic scene classification data set of 2017 IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events(DCASE). On the evaluation set, an accuracy of 64.0 % from bidirectional gated recurrent neural networks is obtained when fusing the spectrogram and the bump scalogram, which is an improvement on the 61.0 % baseline result provided by the DCASE 2017 organisers. This result shows that extracted bump scalograms are capable of improving the classification accuracy,when fusing with a spectrogram-based system.展开更多
Time-frequency methods are effective tools in identifying the frequency content of a signal and revealing its timevariant features.This paper presents the use of instantaneous features(i.e.,instantaneous energy and si...Time-frequency methods are effective tools in identifying the frequency content of a signal and revealing its timevariant features.This paper presents the use of instantaneous features(i.e.,instantaneous energy and signal phase)of acoustic emission(AE)in the detection of thermal damage to the workpiece in grinding.The low-order frequency moments of a scalogram are used to obtain both the instantaneous energy and the mean frequency at which the signal phase is recovered.The grinding process is monitored using AE for a variety of operating conditions,including regular grinding,grinding at higher cutting speed and larger feed,and small dressing depth of cut.The instantaneous features extracted by the scalogram are compared with the results obtained by the empirical mode decomposition.It has been found that both the instantaneous energy and phase deviation indicate the presence of burn damage and serve as robust and reliable indicators,providing a basis for detecting the grinding burn.展开更多
BACKGROUND The continuous glucose monitoring(CGM)system has become a popular evaluation tool for glucose fluctuation,providing a detailed description of glucose change patterns.We hypothesized that glucose fluctuation...BACKGROUND The continuous glucose monitoring(CGM)system has become a popular evaluation tool for glucose fluctuation,providing a detailed description of glucose change patterns.We hypothesized that glucose fluctuations may contain specific information on differences in glucose change between type 1 diabetes mellitus(T1DM)and type 2 diabetes mellitus(T2DM),despite similarities in change patterns,because of different etiologies.Unlike Fourier transform,continuous wavelet transform(CWT)is able to simultaneously analyze the time and frequency domains of oscillating data.AIM To investigate whether CWT can detect glucose fluctuations in T1DM.METHODS The 60-d and 296-d glucose fluctuation data of patients with T1DM(n=5)and T2DM(n=25)were evaluated respectively.Glucose data obtained every 15 min for 356 d were analyzed.Data were assessed by CWT with Morlet form(n=7)as the mother wavelet.This methodology was employed to search for limited frequency glucose fluctuation in the daily glucose change.The frequency and enclosed area(0.02625 scalogram value)of 18 emerged signals were compared.The specificity for T1DM was evaluated through multiple regression analysis using items that demonstrated significant differences between them as explanatory variables.RESULTS The high frequency at midnight(median:75 Hz,cycle time:19 min)and middle frequency at noon(median:45.5 Hz,cycle time:32 min)were higher in T1DM vs T2DM(median:73 and 44 Hz;P=0.006 and 0.005,respectively).The area of the>100 Hz zone at midnight to forenoon was more frequent and larger in T1DM vs T2DM.In a day,the lower frequency zone(15-35 Hz)was more frequent and the area was larger in T2DM than in T1DM.The threedimensional scatter diagrams,which consist of the time of day,frequency,and area of each signal after CWT,revealed that high frequency signals belonging to T1DM at midnight had a loose distribution of wave cycles that were 17-24 min.Multivariate analysis revealed that the high frequency signal at midnight could characterize T1DM(odds ratio:1.33,95%confidence interval:1.08-1.62;P=0.006).CONCLUSION CWT might be a novel tool for differentiate glucose fluctuation of each type of diabetes mellitus using CGM data.展开更多
基金supported by the German National BMBF IKT2020-Grant(16SV7213)(EmotAsS)the European-Unions Horizon 2020 Research and Innovation Programme(688835)(DE-ENIGMA)the China Scholarship Council(CSC)
文摘Spectrogram representations of acoustic scenes have achieved competitive performance for acoustic scene classification. Yet, the spectrogram alone does not take into account a substantial amount of time-frequency information. In this study, we present an approach for exploring the benefits of deep scalogram representations, extracted in segments from an audio stream. The approach presented firstly transforms the segmented acoustic scenes into bump and morse scalograms, as well as spectrograms; secondly, the spectrograms or scalograms are sent into pre-trained convolutional neural networks; thirdly,the features extracted from a subsequent fully connected layer are fed into(bidirectional) gated recurrent neural networks, which are followed by a single highway layer and a softmax layer;finally, predictions from these three systems are fused by a margin sampling value strategy. We then evaluate the proposed approach using the acoustic scene classification data set of 2017 IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events(DCASE). On the evaluation set, an accuracy of 64.0 % from bidirectional gated recurrent neural networks is obtained when fusing the spectrogram and the bump scalogram, which is an improvement on the 61.0 % baseline result provided by the DCASE 2017 organisers. This result shows that extracted bump scalograms are capable of improving the classification accuracy,when fusing with a spectrogram-based system.
文摘Time-frequency methods are effective tools in identifying the frequency content of a signal and revealing its timevariant features.This paper presents the use of instantaneous features(i.e.,instantaneous energy and signal phase)of acoustic emission(AE)in the detection of thermal damage to the workpiece in grinding.The low-order frequency moments of a scalogram are used to obtain both the instantaneous energy and the mean frequency at which the signal phase is recovered.The grinding process is monitored using AE for a variety of operating conditions,including regular grinding,grinding at higher cutting speed and larger feed,and small dressing depth of cut.The instantaneous features extracted by the scalogram are compared with the results obtained by the empirical mode decomposition.It has been found that both the instantaneous energy and phase deviation indicate the presence of burn damage and serve as robust and reliable indicators,providing a basis for detecting the grinding burn.
文摘BACKGROUND The continuous glucose monitoring(CGM)system has become a popular evaluation tool for glucose fluctuation,providing a detailed description of glucose change patterns.We hypothesized that glucose fluctuations may contain specific information on differences in glucose change between type 1 diabetes mellitus(T1DM)and type 2 diabetes mellitus(T2DM),despite similarities in change patterns,because of different etiologies.Unlike Fourier transform,continuous wavelet transform(CWT)is able to simultaneously analyze the time and frequency domains of oscillating data.AIM To investigate whether CWT can detect glucose fluctuations in T1DM.METHODS The 60-d and 296-d glucose fluctuation data of patients with T1DM(n=5)and T2DM(n=25)were evaluated respectively.Glucose data obtained every 15 min for 356 d were analyzed.Data were assessed by CWT with Morlet form(n=7)as the mother wavelet.This methodology was employed to search for limited frequency glucose fluctuation in the daily glucose change.The frequency and enclosed area(0.02625 scalogram value)of 18 emerged signals were compared.The specificity for T1DM was evaluated through multiple regression analysis using items that demonstrated significant differences between them as explanatory variables.RESULTS The high frequency at midnight(median:75 Hz,cycle time:19 min)and middle frequency at noon(median:45.5 Hz,cycle time:32 min)were higher in T1DM vs T2DM(median:73 and 44 Hz;P=0.006 and 0.005,respectively).The area of the>100 Hz zone at midnight to forenoon was more frequent and larger in T1DM vs T2DM.In a day,the lower frequency zone(15-35 Hz)was more frequent and the area was larger in T2DM than in T1DM.The threedimensional scatter diagrams,which consist of the time of day,frequency,and area of each signal after CWT,revealed that high frequency signals belonging to T1DM at midnight had a loose distribution of wave cycles that were 17-24 min.Multivariate analysis revealed that the high frequency signal at midnight could characterize T1DM(odds ratio:1.33,95%confidence interval:1.08-1.62;P=0.006).CONCLUSION CWT might be a novel tool for differentiate glucose fluctuation of each type of diabetes mellitus using CGM data.
