The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Shiwen Deng - One of the best experts on this subject based on the ideXlab platform.
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heart sound classification based on scaled Spectrogram and tensor decomposition
Expert Systems With Applications, 2017Co-Authors: Wenjie Zhang, Jiqing Han, Shiwen DengAbstract:First, the Spectrograms of heart cycles are scaled for comparison.Second, tensor decomposition is utilized to the scaled Spectrograms.Third, the intrinsic structure information of scaled Spectrograms is extracted.Fourth, more useful physiological and pathological information is reserved.Fifth, the extracted features are more discriminative. Heart sound signal analysis is an effective and convenient method for the preliminary diagnosis of heart disease. However, automatic heart sound classification is still a challenging problem which mainly reflected in heart sound segmentation and feature extraction from the corresponding segmentation results. In order to extract more discriminative features for heart sound classification, a scaled Spectrogram and tensor decomposition based method was proposed in this study. In the proposed method, the Spectrograms of the detected heart cycles are first scaled to a fixed size. Then a dimension reduction process of the scaled Spectrograms is performed to extract the most discriminative features. During the dimension reduction process, the intrinsic structure of the scaled Spectrograms, which contains important physiological and pathological information of the heart sound signals, is extracted using tensor decomposition method. As a result, the extracted features are more discriminative. Finally, the classification task is completed by support vector machine (SVM). Moreover, the proposed method is evaluated on three public datasets offered by the PASCAL classifying heart sounds challenge and 2016 PhysioNet challenge. The results show that the proposed method is competitive.
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Heart sound classification based on scaled Spectrogram and partial least squares regression
Biomedical Signal Processing and Control, 2017Co-Authors: Wenjie Zhang, Jiqing Han, Shiwen DengAbstract:Abstract Phonocardiogram (PCG) signal analysis is an effective and convenient method for the preliminary diagnosis of heart disease. In this study, a scaled Spectrogram and partial least squares regression (PLSR) based method was proposed for the classification of PCG signals. Proposed method is mainly comprised of four stages, namely as being heart cycle estimation, Spectrogram scaling, dimension reduction and classification. At the heart cycle estimation stage, the short time average magnitude difference of the Shannon energy envelope is applied. Then the Spectrogram of the obtained heart cycle is calculated for feature extraction. However, the sizes of the Spectrograms between different PCG signals are usually not the same. In order to overcome the difficulty of direct comparison, the bilinear interpolation is used for the Spectrogram to get the scaled Spectrogram with a fixed size. Nevertheless, the scaled Spectrogram contains a large quantity of redundant and irrelevant information. To extract the most relevant features from the scaled Spectrogram, we adopt the PLSR to reduce the dimension of the scaled Spectrograms. Since PLSR has the advantage of using the category information during the dimension reduction process, the extracted features are more discriminative. Then the classification results are obtained via support vector machine (SVM). The proposed method is evaluated on two public datasets offered by the PASCAL classifying heart sounds challenge, and the results are compared to those obtained using the best methods in the challenge, thereby proving the effectiveness of our method.
M. Muller - One of the best experts on this subject based on the ideXlab platform.
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Spectrogram ANALYSIS OF ANIMAL SOUND PRODUCTION
Bioacoustics, 2008Co-Authors: Coen P. H. Elemans, K. Heeck, M. MullerAbstract:ABSTRACT Spectrograms visualise the time-frequency content of a signal. They are commonly used to analyse animal vocalisations. Here, we analyse how far we can deduce the mechanical origin of sound generation and modulation from the Spectrogram. We investigate the relationship between simple mathematical events such as transients, harmonics, amplitude- and frequency modulation and the resulting structures in Spectrograms. This approach yields not only convenient statistical description, but also aids in formulating hypotheses about the underlying mathematical mechanisms. We then discuss to what extent it is possible to invert our analysis and relate structures in Spectrograms back to the underlying mathematical and mechanical events using two exemplary approaches: (a) we analyse the Spectrogram of a vocalisation of the Bearded Vulture and postulate hypotheses on the mathematical origin of the signal. Furthermore, we synthesise the signal using the simple mathematical principles presented earlier; (b) we u...
Ye Wang - One of the best experts on this subject based on the ideXlab platform.
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subspectralnet using sub Spectrogram based convolutional neural networks for acoustic scene classification
International Conference on Acoustics Speech and Signal Processing, 2019Co-Authors: Sai Samarth R Phaye, Emmanouil Benetos, Ye WangAbstract:Acoustic Scene Classification (ASC) is one of the core research problems in the field of Computational Sound Scene Analysis. In this work, we present SubSpectralNet, a novel model which captures discriminative features by incorporating frequency band-level differences to model soundscapes. Using mel-Spectrograms, we propose the idea of using band-wise crops of the input time-frequency representations and train a convolutional neural network (CNN) on the same. We also propose a modification in the training method for more efficient learning of the CNN models. We first give a motivation for using sub-Spectrograms by giving intuitive and statistical analyses and finally we develop a sub-Spectrogram based CNN architecture for ASC. The system is evaluated on the public ASC development dataset provided for the "Detection and Classification of Acoustic Scenes and Events" (DCASE) 2018 Challenge. Our best model achieves an improvement of +14% in terms of classification accuracy with respect to the DCASE 2018 baseline system. Code and figures are available at https://github.com/ssrp/SubSpectralNet
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subspectralnet using sub Spectrogram based convolutional neural networks for acoustic scene classification
arXiv: Sound, 2018Co-Authors: Sai Samarth R Phaye, Emmanouil Benetos, Ye WangAbstract:Acoustic Scene Classification (ASC) is one of the core research problems in the field of Computational Sound Scene Analysis. In this work, we present SubSpectralNet, a novel model which captures discriminative features by incorporating frequency band-level differences to model soundscapes. Using mel-Spectrograms, we propose the idea of using band-wise crops of the input time-frequency representations and train a convolutional neural network (CNN) on the same. We also propose a modification in the training method for more efficient learning of the CNN models. We first give a motivation for using sub-Spectrograms by giving intuitive and statistical analyses and finally we develop a sub-Spectrogram based CNN architecture for ASC. The system is evaluated on the public ASC development dataset provided for the "Detection and Classification of Acoustic Scenes and Events" (DCASE) 2018 Challenge. Our best model achieves an improvement of +14% in terms of classification accuracy with respect to the DCASE 2018 baseline system. Code and figures are available at this https URL
Douglas O'shaughnessy - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - Segmentation of a speech Spectrogram using mathematical morphology
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: R. Steinberg, Douglas O'shaughnessyAbstract:It has been shown that speech Spectrograms can be read by trained experts. In this work, we regard the speech Spectrogram image as a written text in some unknown language and perform segmentation in order to capture the energy associated with each formant. We propose an algorithm based on Mathematical Morphology operators and mainly on the watershed transform. The result is robust segmentation for wideband speech Spectrograms that can be later used for automatic speech recognition. We show results of experimental runs for different phoneme classes.
Wenjie Zhang - One of the best experts on this subject based on the ideXlab platform.
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heart sound classification based on scaled Spectrogram and tensor decomposition
Expert Systems With Applications, 2017Co-Authors: Wenjie Zhang, Jiqing Han, Shiwen DengAbstract:First, the Spectrograms of heart cycles are scaled for comparison.Second, tensor decomposition is utilized to the scaled Spectrograms.Third, the intrinsic structure information of scaled Spectrograms is extracted.Fourth, more useful physiological and pathological information is reserved.Fifth, the extracted features are more discriminative. Heart sound signal analysis is an effective and convenient method for the preliminary diagnosis of heart disease. However, automatic heart sound classification is still a challenging problem which mainly reflected in heart sound segmentation and feature extraction from the corresponding segmentation results. In order to extract more discriminative features for heart sound classification, a scaled Spectrogram and tensor decomposition based method was proposed in this study. In the proposed method, the Spectrograms of the detected heart cycles are first scaled to a fixed size. Then a dimension reduction process of the scaled Spectrograms is performed to extract the most discriminative features. During the dimension reduction process, the intrinsic structure of the scaled Spectrograms, which contains important physiological and pathological information of the heart sound signals, is extracted using tensor decomposition method. As a result, the extracted features are more discriminative. Finally, the classification task is completed by support vector machine (SVM). Moreover, the proposed method is evaluated on three public datasets offered by the PASCAL classifying heart sounds challenge and 2016 PhysioNet challenge. The results show that the proposed method is competitive.
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Heart sound classification based on scaled Spectrogram and partial least squares regression
Biomedical Signal Processing and Control, 2017Co-Authors: Wenjie Zhang, Jiqing Han, Shiwen DengAbstract:Abstract Phonocardiogram (PCG) signal analysis is an effective and convenient method for the preliminary diagnosis of heart disease. In this study, a scaled Spectrogram and partial least squares regression (PLSR) based method was proposed for the classification of PCG signals. Proposed method is mainly comprised of four stages, namely as being heart cycle estimation, Spectrogram scaling, dimension reduction and classification. At the heart cycle estimation stage, the short time average magnitude difference of the Shannon energy envelope is applied. Then the Spectrogram of the obtained heart cycle is calculated for feature extraction. However, the sizes of the Spectrograms between different PCG signals are usually not the same. In order to overcome the difficulty of direct comparison, the bilinear interpolation is used for the Spectrogram to get the scaled Spectrogram with a fixed size. Nevertheless, the scaled Spectrogram contains a large quantity of redundant and irrelevant information. To extract the most relevant features from the scaled Spectrogram, we adopt the PLSR to reduce the dimension of the scaled Spectrograms. Since PLSR has the advantage of using the category information during the dimension reduction process, the extracted features are more discriminative. Then the classification results are obtained via support vector machine (SVM). The proposed method is evaluated on two public datasets offered by the PASCAL classifying heart sounds challenge, and the results are compared to those obtained using the best methods in the challenge, thereby proving the effectiveness of our method.
