Noise reduction in biomedical speech signal processing based on time and frequency Kalman filtering combined with spectral subtraction

Silva, Leandro Aureliano da; Joaquim, Marcelo Basilio

doi:10.1016/j.compeleceng.2007.10.007

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…As there are various noises in the pig farming environment [ 17 ], it is very important to select a reasonable filtering algorithm for the identification of pig sounds. In this study, the Kalman filtering algorithm was used to reduce the noises of the collected pig sounds [ 18 , 19 ], with the main sources of noise being pig pen exhaust fan noise, birds singing and machinery buzzing. It takes the linear minimum mean square error as the optimal estimation criterion and uses the state-space representation to establish the state equation of signals and noises.…”

Section: Methodsmentioning

confidence: 99%

Research on Pig Sound Recognition Based on Deep Neural Network and Hidden Markov Models

Pan,

Li,

Zhou

et al. 2024

Sensors

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In order to solve the problem of low recognition accuracy of traditional pig sound recognition methods, deep neural network (DNN) and Hidden Markov Model (HMM) theory were used as the basis of pig sound signal recognition in this study. In this study, the sounds made by 10 landrace pigs during eating, estrus, howling, humming and panting were collected and preprocessed by Kalman filtering and an improved endpoint detection algorithm based on empirical mode decomposition-Teiger energy operator (EMD-TEO) cepstral distance. The extracted 39-dimensional mel-frequency cepstral coefficients (MFCCs) were then used as a dataset for network learning and recognition to build a DNN- and HMM-based sound recognition model for pig states. The results show that in the pig sound dataset, the recognition accuracy of DNN-HMM reaches 83%, which is 22% and 17% higher than that of the baseline models HMM and GMM-HMM, and possesses a better recognition effect. In a sub-dataset of the publicly available dataset AudioSet, DNN-HMM achieves a recognition accuracy of 79%, which is 8% and 4% higher than the classical models SVM and ResNet18, respectively, with better robustness.

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Section: Methodsmentioning

confidence: 99%

Research on Pig Sound Recognition Based on Deep Neural Network and Hidden Markov Models

Pan,

Li,

Zhou

et al. 2024

Sensors

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“…The field of speech processing and development of speech recognition systems have received considerable attention during the last decades. With the availability of portable phones and analyzing methods involving traditional digital signal processing approaches such as hidden Markov models, Kalman filter, short-time frequency analysis and wavelet transforms are successfully used for both speech enhancement and speech recognition applications [4,5,6,7,8,9,10,11]. Scientific research on vocal recordings of patients that suffer from Parkinson's disease are not abundant.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Parkinson’s Disease by Boosted Neural Networks

Can

2013

scjournal

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A boosting by filtering technique for neural network systems with back propagation together with a majority voting scheme is presented in this paper. Previous research with regards to predict the presence of Parkinson's Disease has shown accuracy rates up to 92.9% [1] but it comes with a cost of reduced prediction accuracy of the minority class. The designed neural network system boosted by filtering in this article presents a significant increase of robustness and it is shown that by majority voting of the parallel networks, recognition rates reach to > 90 in a imbalanced 3:1 imbalanced class distribution Parkinson's Disease data set.

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“…With the availability of portable phones and analyzing methods involving traditional digital signal processing approaches such as hidden Markov models, Kalman filter, short-time frequency analysis and wavelet transforms are successfully used for both speech enhancement and speech recognition applications [4,5,6,7,8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Neural Networks to Diagnose the Parkinson’s Disease

Can

2013

scjournal

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To identify the presence of Parkinson's disease, a neural network system with back propagation together with a majority voting scheme is presented in this paper. The data used has an imparity of the ratio 3:1. Previous research with regards to predict the presence of the disease has shown accuracy rates up to 92.9% [1] but it comes with a cost of reduced prediction accuracy of the small class. The designed neural network system is boosted by filtering, and this causes a significant increase of robustness. It is also shown that by majority voting of eleven parallel networks, recognition rates reached to > 90 in spite of 3:1 imbalanced class distribution of the Parkinson's disease data set.

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Noise reduction in biomedical speech signal processing based on time and frequency Kalman filtering combined with spectral subtraction

Cited by 8 publications

References 5 publications

Research on Pig Sound Recognition Based on Deep Neural Network and Hidden Markov Models

Research on Pig Sound Recognition Based on Deep Neural Network and Hidden Markov Models

Diagnosis of Parkinson’s Disease by Boosted Neural Networks

Neural Networks to Diagnose the Parkinson’s Disease

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