Adaptive bit-loading is a key technology in high speed power line communications with the Orthogonal Frequency Division Multiplexing (OFDM) modulation technology. According to the real situation of the transmitting po...Adaptive bit-loading is a key technology in high speed power line communications with the Orthogonal Frequency Division Multiplexing (OFDM) modulation technology. According to the real situation of the transmitting power spectrum limited in high speed power line communications, this paper explored the adaptive bit loading algorithm to maximize transmission bit number when transmitting power spectral density and bit error rate are not exceed upper limit. With the characteristics of the power line channel, first of all, it obtains the optimal bit loading algorithm, and then provides the improved algorithm to reduce the computational complexity. Based on the analysis and simulation, it offers a non-iterative bit allocation algorithm, and finally the simulation shows that this new algorithm can greatly reduce the computational complexity, and the actual bit allocation results close to optimal.展开更多
With the increasing requirement of military and security, the technology of information hiding for speech becomes a hotspot and difficulty in the fields of speech signal processing and in-formation security, which is ...With the increasing requirement of military and security, the technology of information hiding for speech becomes a hotspot and difficulty in the fields of speech signal processing and in-formation security, which is developing rapidly. In order to stand against the stegano-analysis, the paper proposed an optimal information hiding algorithm for speech in the Fractional Fourier Transform (FrFT) domain based on the Minimum Mean Square Error (MMSE) criterion. The results of simulation and experiments show that speech modified by the proposed algorithm has no remarkable changes both in time and frequency domains, which can effectively resist the time and frequency analysis, Otherwise, the algorithm is robust to general signal process attack, and the difference is imperceptible between the original and modified speech.展开更多
Due to the drawbacks in Support Vector Machine(SVM)parameter optimization,an Improved Shuffled Frog Leaping Algorithm(Im-SFLA)was proposed,and the learning ability in practical speech emotion recognition was improved....Due to the drawbacks in Support Vector Machine(SVM)parameter optimization,an Improved Shuffled Frog Leaping Algorithm(Im-SFLA)was proposed,and the learning ability in practical speech emotion recognition was improved.Firstly,we introduced Simulated Annealing(SA),Immune Vaccination(Iv),Gaussian mutation and chaotic disturbance into the basic SFLA,which bManced the search efficiency and population diversity effectively.Secondly,Im-SFLA Was applied to the optimization of SVM parameters,and an Im-SFLA-SVM method Was proposed.Thirdly,the acoustic features of practical speech emotion,such aS ridgetiness,were analyzed.The pitch frequency,short-term energy,formant frequency and chaotic characteristics were analyzed corresponding to different emotion categories,and we constructed a 144-dimensional emotion feature vector for recognition and reduced to 4-dimension by adopting Linear Discriminant Analysis(LDA) Finally,the Im-SFLA-SVM method Was tested on the practical speech emotion database,and the recognition results were compared with Shuffled Frog Leaping Algorithm optimization-SVM(SFLA-SVM)method,Particle Swarm Optimization algorithm optimization-SVM(PSo-SVM) method,basic SVM,Gaussian Mixture Model(GMM)method and Back Propagation(BP)neural network method.The experimentM resuits showed that the average recognition rate of Im-SFLA-SVM method was 77.8%,which had improved 1.7%,2.7%,3.4%,4.7%and 7.8%respectively,compared with the other methods.The recognition of fidgetiness was significantly improve,thus verifying that Im-SFLA was an effective SVM parameter selection method,and the Im-SFLA-SVM method may significantly improve the practical speech emotion recognition.展开更多
基金Supported by the National Natural Science Foundation Project (No. 60872073, No. 60975017, and No. 51075068)Doctoral Fund of Education Ministry (No. 2011009213-0004)
文摘Adaptive bit-loading is a key technology in high speed power line communications with the Orthogonal Frequency Division Multiplexing (OFDM) modulation technology. According to the real situation of the transmitting power spectrum limited in high speed power line communications, this paper explored the adaptive bit loading algorithm to maximize transmission bit number when transmitting power spectral density and bit error rate are not exceed upper limit. With the characteristics of the power line channel, first of all, it obtains the optimal bit loading algorithm, and then provides the improved algorithm to reduce the computational complexity. Based on the analysis and simulation, it offers a non-iterative bit allocation algorithm, and finally the simulation shows that this new algorithm can greatly reduce the computational complexity, and the actual bit allocation results close to optimal.
基金Supported by the National Natural Science Foundation of China (No. 60472058, No. 60975017)Jiangsu Provincial Natural Science Foundation (No. BK2008291)
文摘With the increasing requirement of military and security, the technology of information hiding for speech becomes a hotspot and difficulty in the fields of speech signal processing and in-formation security, which is developing rapidly. In order to stand against the stegano-analysis, the paper proposed an optimal information hiding algorithm for speech in the Fractional Fourier Transform (FrFT) domain based on the Minimum Mean Square Error (MMSE) criterion. The results of simulation and experiments show that speech modified by the proposed algorithm has no remarkable changes both in time and frequency domains, which can effectively resist the time and frequency analysis, Otherwise, the algorithm is robust to general signal process attack, and the difference is imperceptible between the original and modified speech.
基金supported by the National Nature Science Foundation(61231002,61273266,51075068)the Doctoral Fund of Ministry of Education of China(20110092130004)+1 种基金the Postdoctoral Fund of Ministry of Education of China(2012M520973)the Open Research Foundation of Key Laboratory(B) of Underwater Acoustic Signal Processing of Ministry of Education of Southeast University under Grant(UASP1202)
文摘Due to the drawbacks in Support Vector Machine(SVM)parameter optimization,an Improved Shuffled Frog Leaping Algorithm(Im-SFLA)was proposed,and the learning ability in practical speech emotion recognition was improved.Firstly,we introduced Simulated Annealing(SA),Immune Vaccination(Iv),Gaussian mutation and chaotic disturbance into the basic SFLA,which bManced the search efficiency and population diversity effectively.Secondly,Im-SFLA Was applied to the optimization of SVM parameters,and an Im-SFLA-SVM method Was proposed.Thirdly,the acoustic features of practical speech emotion,such aS ridgetiness,were analyzed.The pitch frequency,short-term energy,formant frequency and chaotic characteristics were analyzed corresponding to different emotion categories,and we constructed a 144-dimensional emotion feature vector for recognition and reduced to 4-dimension by adopting Linear Discriminant Analysis(LDA) Finally,the Im-SFLA-SVM method Was tested on the practical speech emotion database,and the recognition results were compared with Shuffled Frog Leaping Algorithm optimization-SVM(SFLA-SVM)method,Particle Swarm Optimization algorithm optimization-SVM(PSo-SVM) method,basic SVM,Gaussian Mixture Model(GMM)method and Back Propagation(BP)neural network method.The experimentM resuits showed that the average recognition rate of Im-SFLA-SVM method was 77.8%,which had improved 1.7%,2.7%,3.4%,4.7%and 7.8%respectively,compared with the other methods.The recognition of fidgetiness was significantly improve,thus verifying that Im-SFLA was an effective SVM parameter selection method,and the Im-SFLA-SVM method may significantly improve the practical speech emotion recognition.
