Pub Date : 2007-12-01DOI: 10.1109/ISSPIT.2007.4458170
E. Hellerud, U. Svensson
A system for streaming lossless audio with low delay over IP networks is presented. To achieve error robustness, the signal is divided into a base and an enhancement layer when the network is approaching congestion. The base layer is perceptually encoded using a time-varying pre- and postfilter, and this layer is transported using a high priority traffic class in a Differentiated Services (DiffServ) network. The enhancement layer is the difference between the original signal and base layer, and is transmitted using a regular Best Effort traffic class. In our experiments the system delay is just 256 samples, and it can be seen that the layering only introduces moderate amounts of redundancy, while improving the error resilience significantly.
{"title":"Robust Transmission of Lossless Audio with Low Delay over IP Networks","authors":"E. Hellerud, U. Svensson","doi":"10.1109/ISSPIT.2007.4458170","DOIUrl":"https://doi.org/10.1109/ISSPIT.2007.4458170","url":null,"abstract":"A system for streaming lossless audio with low delay over IP networks is presented. To achieve error robustness, the signal is divided into a base and an enhancement layer when the network is approaching congestion. The base layer is perceptually encoded using a time-varying pre- and postfilter, and this layer is transported using a high priority traffic class in a Differentiated Services (DiffServ) network. The enhancement layer is the difference between the original signal and base layer, and is transmitted using a regular Best Effort traffic class. In our experiments the system delay is just 256 samples, and it can be seen that the layering only introduces moderate amounts of redundancy, while improving the error resilience significantly.","PeriodicalId":299267,"journal":{"name":"2007 IEEE International Symposium on Signal Processing and Information Technology","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133171627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-12-01DOI: 10.1109/ISSPIT.2007.4458159
F. Awan, S. Marshall, J. Soraghan, M. N. Arbab
This paper compares different robust techniques used for imperceptible watermarking, which are resistant to geometric and signal processing attacks. The dual tree complex wavelet transform (DT CWT) provides a means of producing solutions for robust watermarking. Complex wavelet transforms use Gabor real time filters having the properties of shift invariance and directional selectivity. This produces a considerable reduction in the complexity making the DT CWT an ideal solution for real time watermarking. The discrete cosine transform (DCT) has also been proposed for robust watermarking because of its resistance to geometrical attacks. In this paper a hybrid system comprising the DCT and DT CWT is used in order to produce a more robust technique for watermarking. In the process of embedding a watermark, the properties of a human visual system (HVS) are also considered. This paper compares the watermarking process for DT CWT, DT CWT with DCT and discrete wavelets transform (DWT) in producing robust watermarks. This paper demonstrates the superior performance in the presence of geometric and signal processing attacks of the hybrid DT CWT-DCT technique.
{"title":"Performance of a Hybrid DCT - DT CWT Digital Watermarking against Geometric and Signal Processing Attacks","authors":"F. Awan, S. Marshall, J. Soraghan, M. N. Arbab","doi":"10.1109/ISSPIT.2007.4458159","DOIUrl":"https://doi.org/10.1109/ISSPIT.2007.4458159","url":null,"abstract":"This paper compares different robust techniques used for imperceptible watermarking, which are resistant to geometric and signal processing attacks. The dual tree complex wavelet transform (DT CWT) provides a means of producing solutions for robust watermarking. Complex wavelet transforms use Gabor real time filters having the properties of shift invariance and directional selectivity. This produces a considerable reduction in the complexity making the DT CWT an ideal solution for real time watermarking. The discrete cosine transform (DCT) has also been proposed for robust watermarking because of its resistance to geometrical attacks. In this paper a hybrid system comprising the DCT and DT CWT is used in order to produce a more robust technique for watermarking. In the process of embedding a watermark, the properties of a human visual system (HVS) are also considered. This paper compares the watermarking process for DT CWT, DT CWT with DCT and discrete wavelets transform (DWT) in producing robust watermarks. This paper demonstrates the superior performance in the presence of geometric and signal processing attacks of the hybrid DT CWT-DCT technique.","PeriodicalId":299267,"journal":{"name":"2007 IEEE International Symposium on Signal Processing and Information Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133214922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-12-01DOI: 10.1109/ISSPIT.2007.4458124
Saikat Chatterjee, T. Sreenivas
We address the issue of rate-distortion (R/D) performance optimality of the recently proposed switched split vector quantization (SSVQ) method. The distribution of the source is modeled using Gaussian mixture density and thus, the non-parametric SSVQ is analyzed in a parametric model based framework for achieving optimum R/D performance. Using high rate quantization theory, we derive the optimum bit allocation formulae for the intra-cluster split vector quantizer (SVQ) and the inter-cluster switching. For the wide-band speech line spectrum frequency (LSF) parameter quantization, it is shown that the Gaussian mixture model (GMM) based parametric SSVQ method provides 1 bit/vector advantage over the non-parametric SSVQ method.
{"title":"Gaussian Mixture Model Based Switched Split Vector Quantization of LSF Parameters","authors":"Saikat Chatterjee, T. Sreenivas","doi":"10.1109/ISSPIT.2007.4458124","DOIUrl":"https://doi.org/10.1109/ISSPIT.2007.4458124","url":null,"abstract":"We address the issue of rate-distortion (R/D) performance optimality of the recently proposed switched split vector quantization (SSVQ) method. The distribution of the source is modeled using Gaussian mixture density and thus, the non-parametric SSVQ is analyzed in a parametric model based framework for achieving optimum R/D performance. Using high rate quantization theory, we derive the optimum bit allocation formulae for the intra-cluster split vector quantizer (SVQ) and the inter-cluster switching. For the wide-band speech line spectrum frequency (LSF) parameter quantization, it is shown that the Gaussian mixture model (GMM) based parametric SSVQ method provides 1 bit/vector advantage over the non-parametric SSVQ method.","PeriodicalId":299267,"journal":{"name":"2007 IEEE International Symposium on Signal Processing and Information Technology","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116445784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-12-01DOI: 10.1109/ISSPIT.2007.4458078
Khaled Mayyas
An Affine Projection (AP) adaptive algorithm employing a new adaptive step-size control equation is proposed. The variable step-size (VSS) is an efficient esti mation of a theoretical optimal one based on the minimization of the mean-square error (MSE) at each time instant. As a result, improvement in convergence speed is attained in early stages of convergence with small misadjustment near the optimum. The algorithm enhanced performance characteristics are verified by simulation examples.
{"title":"An Affine Projection Algorithm with an Adaptive Step-Size Equation","authors":"Khaled Mayyas","doi":"10.1109/ISSPIT.2007.4458078","DOIUrl":"https://doi.org/10.1109/ISSPIT.2007.4458078","url":null,"abstract":"An Affine Projection (AP) adaptive algorithm employing a new adaptive step-size control equation is proposed. The variable step-size (VSS) is an efficient esti mation of a theoretical optimal one based on the minimization of the mean-square error (MSE) at each time instant. As a result, improvement in convergence speed is attained in early stages of convergence with small misadjustment near the optimum. The algorithm enhanced performance characteristics are verified by simulation examples.","PeriodicalId":299267,"journal":{"name":"2007 IEEE International Symposium on Signal Processing and Information Technology","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115888188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-12-01DOI: 10.1109/ISSPIT.2007.4458157
Ning Wang, P. C. Ching, Nengheng Zheng, Tan Lee
Motivated by the mechanism of speech production, we present a novel idea of using source-tract features in training speaker models for recognition. By considering the severe degradation occurring when a speaker recognition system operates under noisy environment, which could well be due to the missing of speaker-distinctive information, we propose a robust feature estimation method that can capture the source and tract related speech properties from noisy input speech utterances. As a simple yet useful speech enhancement technique, spectral subtractive-type algorithm is employed to remove the additive noise prior to feature extraction process. It is shown through analytical derivation as well as simulation that the proposed feature estimation method leads to robust recognition performance, especially for very low signal-to-noise ratios. In the context of Gaussian mixture model-based speaker recognition with the presence of additive white Gaussian noise in the input utterances, the new approach produces consistent reduction of both identification error rate and equal error rate at signal-to-noise ratios ranging from 0 dB to 15 dB.
{"title":"Robust Speaker Recognition Using Both Vocal Source and Vocal Tract Features Estimated from Noisy Input Utterances","authors":"Ning Wang, P. C. Ching, Nengheng Zheng, Tan Lee","doi":"10.1109/ISSPIT.2007.4458157","DOIUrl":"https://doi.org/10.1109/ISSPIT.2007.4458157","url":null,"abstract":"Motivated by the mechanism of speech production, we present a novel idea of using source-tract features in training speaker models for recognition. By considering the severe degradation occurring when a speaker recognition system operates under noisy environment, which could well be due to the missing of speaker-distinctive information, we propose a robust feature estimation method that can capture the source and tract related speech properties from noisy input speech utterances. As a simple yet useful speech enhancement technique, spectral subtractive-type algorithm is employed to remove the additive noise prior to feature extraction process. It is shown through analytical derivation as well as simulation that the proposed feature estimation method leads to robust recognition performance, especially for very low signal-to-noise ratios. In the context of Gaussian mixture model-based speaker recognition with the presence of additive white Gaussian noise in the input utterances, the new approach produces consistent reduction of both identification error rate and equal error rate at signal-to-noise ratios ranging from 0 dB to 15 dB.","PeriodicalId":299267,"journal":{"name":"2007 IEEE International Symposium on Signal Processing and Information Technology","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115096472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-12-01DOI: 10.1109/ISSPIT.2007.4458141
Ata Abbasi, Farhad Pashakhanlou, B. Vahdat
The Block method approach to solve EIT problem leads to an exact solution if the measurements are done without error. Non-iterative method is a feasible approach on solving 3D EIT forward problem. However, the effect of the measurement error has not been considered in this method yet. In this article, the 3D model of EIT with block method has been considered. The required equations to solve the forward problem are then generated. To solve the forward problem, non-iterative method has been employed. Effect of the measurement error on forward problem for a 3D model of EIT are generated. It has been shown that for a sample 3D model, measurement error can propagate exponentially.
{"title":"Error Propagation in Non-Iterative EIT Block Method","authors":"Ata Abbasi, Farhad Pashakhanlou, B. Vahdat","doi":"10.1109/ISSPIT.2007.4458141","DOIUrl":"https://doi.org/10.1109/ISSPIT.2007.4458141","url":null,"abstract":"The Block method approach to solve EIT problem leads to an exact solution if the measurements are done without error. Non-iterative method is a feasible approach on solving 3D EIT forward problem. However, the effect of the measurement error has not been considered in this method yet. In this article, the 3D model of EIT with block method has been considered. The required equations to solve the forward problem are then generated. To solve the forward problem, non-iterative method has been employed. Effect of the measurement error on forward problem for a 3D model of EIT are generated. It has been shown that for a sample 3D model, measurement error can propagate exponentially.","PeriodicalId":299267,"journal":{"name":"2007 IEEE International Symposium on Signal Processing and Information Technology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130594235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-12-01DOI: 10.1109/ISSPIT.2007.4458037
M. M. Tantawy, M. El-Yazeed, N.S. Abdel, M.M. El-Henawy
In this paper we propose a modification to a fast vector quantization algorithm based on nearest partition set search. The fast algorithm searches the codebook to find the nearest set of codevectors for each codevector in the codebook. The nearest set of codevectors is called nearest set partition (NPS) which calculated each iteration. During each iteration the fast algorithm searches the NPS instead of searching the codebook which save training time. The NPS algorithm does well but with large codebook the saved timed consumed in calculating the NPS. So we proposed a modified algorithm to overcome this problem. The experimental results indicate that variation of NPS is slow with iteration. According to our results the calculation of NPS in each iteration is not necessary which save more training time without affecting the codebook quality.
{"title":"A Modified Fast Vector Quantization Algorithm Based on Nearest Partition Set Search","authors":"M. M. Tantawy, M. El-Yazeed, N.S. Abdel, M.M. El-Henawy","doi":"10.1109/ISSPIT.2007.4458037","DOIUrl":"https://doi.org/10.1109/ISSPIT.2007.4458037","url":null,"abstract":"In this paper we propose a modification to a fast vector quantization algorithm based on nearest partition set search. The fast algorithm searches the codebook to find the nearest set of codevectors for each codevector in the codebook. The nearest set of codevectors is called nearest set partition (NPS) which calculated each iteration. During each iteration the fast algorithm searches the NPS instead of searching the codebook which save training time. The NPS algorithm does well but with large codebook the saved timed consumed in calculating the NPS. So we proposed a modified algorithm to overcome this problem. The experimental results indicate that variation of NPS is slow with iteration. According to our results the calculation of NPS in each iteration is not necessary which save more training time without affecting the codebook quality.","PeriodicalId":299267,"journal":{"name":"2007 IEEE International Symposium on Signal Processing and Information Technology","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127898967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-12-01DOI: 10.1109/ISSPIT.2007.4458076
Aboozar Taherkhani, Ali Seyyedsalehi, Arash Mohammadi, Mohammad Hasan, Moradi
Acoustic voice analysis is an effective, cheap and non-invasive tool that can be used to confirm the initial diagnosis and provides an objective determination of the impairment. The nonlinearities of the voice source mechanisms may cause the existence of chaos in human voice production. Voice pathology can cause to addition colored noise to voice wave. Added noise to a chaotic signal causes reduction of the deterministic property and therefore increases correlation dimension of signal. Surrogate data analysis can measure this deviation and give a criterion for amount of noise added to the chaotic signal. By using this criterion a threshold level is set to separate disordered voice from normal voice and 95% accuracy is achieved.
{"title":"Nonlinear Signal Processing for Voice Disorder Detection by Using Modified GP Algorithm and Surrogate Data Analysis","authors":"Aboozar Taherkhani, Ali Seyyedsalehi, Arash Mohammadi, Mohammad Hasan, Moradi","doi":"10.1109/ISSPIT.2007.4458076","DOIUrl":"https://doi.org/10.1109/ISSPIT.2007.4458076","url":null,"abstract":"Acoustic voice analysis is an effective, cheap and non-invasive tool that can be used to confirm the initial diagnosis and provides an objective determination of the impairment. The nonlinearities of the voice source mechanisms may cause the existence of chaos in human voice production. Voice pathology can cause to addition colored noise to voice wave. Added noise to a chaotic signal causes reduction of the deterministic property and therefore increases correlation dimension of signal. Surrogate data analysis can measure this deviation and give a criterion for amount of noise added to the chaotic signal. By using this criterion a threshold level is set to separate disordered voice from normal voice and 95% accuracy is achieved.","PeriodicalId":299267,"journal":{"name":"2007 IEEE International Symposium on Signal Processing and Information Technology","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124628267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-12-01DOI: 10.1109/ISSPIT.2007.4458099
S. Z. Mahmoodabadi, J. Alirezaie, P. Babyn
An application of wavelet packet is presented for electrocardiogram (ECG) and magnetic resonance spectroscopy (MRS) characteristics detection in this study. A fully automated system is developed to detect the "R" peaks which are beat designators and are used consequently to locate other ECG characteristics. They include "P", "Q", "S" and "T" waves along with "ST" segment shift. The peaks and the area under the peaks of MRS signals are also detected. The Daubechies wavelets are selected as base processing filters. Frequency ordered wavelet packets (FOWPT) is utilized to generate a time-frequency plot of the signal used for further processing. The algorithm is validated on MIT-BIH database. The proposed beat detector achieved sensitivity of 99.18%plusmn2.75 and a positive predictivity of 98.00%plusmn4.45. The "P" wave detector achieved sensitivity of 51.69% and a positive predictivity of 53.64%.
{"title":"Bio-signal Characteristics Detection Utilizing Frequency Ordered Wavelet Packets","authors":"S. Z. Mahmoodabadi, J. Alirezaie, P. Babyn","doi":"10.1109/ISSPIT.2007.4458099","DOIUrl":"https://doi.org/10.1109/ISSPIT.2007.4458099","url":null,"abstract":"An application of wavelet packet is presented for electrocardiogram (ECG) and magnetic resonance spectroscopy (MRS) characteristics detection in this study. A fully automated system is developed to detect the \"R\" peaks which are beat designators and are used consequently to locate other ECG characteristics. They include \"P\", \"Q\", \"S\" and \"T\" waves along with \"ST\" segment shift. The peaks and the area under the peaks of MRS signals are also detected. The Daubechies wavelets are selected as base processing filters. Frequency ordered wavelet packets (FOWPT) is utilized to generate a time-frequency plot of the signal used for further processing. The algorithm is validated on MIT-BIH database. The proposed beat detector achieved sensitivity of 99.18%plusmn2.75 and a positive predictivity of 98.00%plusmn4.45. The \"P\" wave detector achieved sensitivity of 51.69% and a positive predictivity of 53.64%.","PeriodicalId":299267,"journal":{"name":"2007 IEEE International Symposium on Signal Processing and Information Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129052324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-12-01DOI: 10.1109/ISSPIT.2007.4458108
K. Suresh, T. Sreenivas
We extend the recently proposed spectral integration based psychoacoustic model for sinusoidal distortions to the MDCT domain. The estimated masking threshold additionally depends on the sub-band spectral flatness measure of the signal which accounts for the non- sinusoidal distortion introduced by masking. The expressions for masking threshold are derived and the validity of the proposed model is established through perceptual transparency test of audio clips. Test results indicate that we do achieve transparent quality reconstruction with the new model. Performance of the model is compared with MPEG psychoacoustic models with respect to the estimated perceptual entropy (PE). The results show that the proposed model predicts a lower PE than other models.
{"title":"Direct MDCT Domain Psychoacoustic Modeling","authors":"K. Suresh, T. Sreenivas","doi":"10.1109/ISSPIT.2007.4458108","DOIUrl":"https://doi.org/10.1109/ISSPIT.2007.4458108","url":null,"abstract":"We extend the recently proposed spectral integration based psychoacoustic model for sinusoidal distortions to the MDCT domain. The estimated masking threshold additionally depends on the sub-band spectral flatness measure of the signal which accounts for the non- sinusoidal distortion introduced by masking. The expressions for masking threshold are derived and the validity of the proposed model is established through perceptual transparency test of audio clips. Test results indicate that we do achieve transparent quality reconstruction with the new model. Performance of the model is compared with MPEG psychoacoustic models with respect to the estimated perceptual entropy (PE). The results show that the proposed model predicts a lower PE than other models.","PeriodicalId":299267,"journal":{"name":"2007 IEEE International Symposium on Signal Processing and Information Technology","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125921285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：