The quality of a multichannel audio signal may be reduced by missing data, which must be recovered before use. The data sets of multichannel audio can be quite large and have more than two axes of variation, such as c...The quality of a multichannel audio signal may be reduced by missing data, which must be recovered before use. The data sets of multichannel audio can be quite large and have more than two axes of variation, such as channel, frame, and feature. To recover missing audio data, we propose a low-rank tensor completion method that is a high-order generalization of matrix completion. First, a multichannel audio signal with missing data is modeled by a three-order tensor. Next, tensor completion is formulated as a convex optimization problem by defining the trace norm of the tensor, and then an augmented Lagrange multiplier method is used for solving the constrained optimization problem. Finally, the missing data is replaced by alternating iteration with a tensor computation. Experiments were conducted to evaluate the effectiveness on data of a 5.1-channel audio signal. The results show that the proposed method outperforms state-of-the-art methods. Moreover, subjective listening tests with MUSHRA(Multiple Stimuli with Hidden Reference and Anchor) indicate that better audio effects were obtained by tensor completion.展开更多
In the discipline of Music Information Retrieval(MIR),categorizing musicfiles according to their genre is a difficult process.Music genre classifica-tion is an important multimedia research domain for classification of mu...In the discipline of Music Information Retrieval(MIR),categorizing musicfiles according to their genre is a difficult process.Music genre classifica-tion is an important multimedia research domain for classification of music data-bases.In the proposed method music genre classification using features obtained from audio data is proposed.The classification is done using features extracted from the audio data of popular online repository namely GTZAN,ISMIR 2004 and Latin Music Dataset(LMD).The features highlight the differences between different musical styles.In the proposed method,feature selection is per-formed using an African Buffalo Optimization(ABO),and the resulting features are employed to classify the audio using Back Propagation Neural Networks(BPNN),Support Vector Machine(SVM),Naïve Bayes,decision tree and kNN classifiers.Performance evaluation reveals that,ABO based feature selection strategy achieves an average accuracy of 82%with mean square error(MSE)of 0.003 when used with neural network classifier.展开更多
基金partially supported by the National Natural Science Foundation of China under Grants No. 61571044, No.61620106002, No.61473041, No.11590772, No.61640012Inner Mongolia Natural Science Foundation under Grants No. 2017MS(LH)0602
文摘The quality of a multichannel audio signal may be reduced by missing data, which must be recovered before use. The data sets of multichannel audio can be quite large and have more than two axes of variation, such as channel, frame, and feature. To recover missing audio data, we propose a low-rank tensor completion method that is a high-order generalization of matrix completion. First, a multichannel audio signal with missing data is modeled by a three-order tensor. Next, tensor completion is formulated as a convex optimization problem by defining the trace norm of the tensor, and then an augmented Lagrange multiplier method is used for solving the constrained optimization problem. Finally, the missing data is replaced by alternating iteration with a tensor computation. Experiments were conducted to evaluate the effectiveness on data of a 5.1-channel audio signal. The results show that the proposed method outperforms state-of-the-art methods. Moreover, subjective listening tests with MUSHRA(Multiple Stimuli with Hidden Reference and Anchor) indicate that better audio effects were obtained by tensor completion.
文摘In the discipline of Music Information Retrieval(MIR),categorizing musicfiles according to their genre is a difficult process.Music genre classifica-tion is an important multimedia research domain for classification of music data-bases.In the proposed method music genre classification using features obtained from audio data is proposed.The classification is done using features extracted from the audio data of popular online repository namely GTZAN,ISMIR 2004 and Latin Music Dataset(LMD).The features highlight the differences between different musical styles.In the proposed method,feature selection is per-formed using an African Buffalo Optimization(ABO),and the resulting features are employed to classify the audio using Back Propagation Neural Networks(BPNN),Support Vector Machine(SVM),Naïve Bayes,decision tree and kNN classifiers.Performance evaluation reveals that,ABO based feature selection strategy achieves an average accuracy of 82%with mean square error(MSE)of 0.003 when used with neural network classifier.
