A Robust Approach For Acoustic Noise Suppression In Speech Using ANFIS

Martínek, Radek; Kelnar, Michal; Vaňuš, Jan; Bilík, Petr; Žídek, Jan

doi:10.2478/jee-2015-0050

Cited by 19 publications

(11 citation statements)

References 20 publications

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“…closed windows, doors, no forced ventilation, • rapid decrease ( Fig. 5), points [19][20][23][24], open window or doors, • gradually ( Fig. 5), points 4-5, 9-10, 11-12, closed windows, doors, no forced ventilation.…”

Section: First Part-use Of Co 2 Sensors For Determining the Presence mentioning

confidence: 99%

“…"Aging in place" for an elderly person is one key element in ambient assisted living (AAL) technologies [1]. For recognition [2][3][4][5][6][7][8][9][10][11][12][13][14][15] and classification of ADL [16,17] are used various mathematical methods such as Hidden Markov Model (HMM), Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM) [18,6], Artificial Neural Networks (ANN) [11] or adaptive-network-based fuzzy inference system (ANFIS) [19,20]. For detection of ADL in SHC it is possible to use RFID [21], PIR [22], CO 2 [23] sensors or presence sensors, on the basis of which probability models of the people's behavior in SH [24] can be built, respecting the privacy [25] of SHC residents [26].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring of the daily living activities in smart home care

Vaňuš

Belesova

Martínek

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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One of the key requirements for technological systems that are used to secure independent housing for seniors in their home environment is monitoring of daily living activities (ADL), their classification, and recognition of routine daily patterns and habits of seniors in Smart Home Care (SHC). To monitor daily living activities, the use of a temperature, CO 2 , humidity sensors, and microphones are described in experiments in this study. The first part of the paper describes the use of CO 2 concentration measurement for detecting and monitoring room´s occupancy in SHC. In second part focuses this paper on the proposal of an implementation of Artificial Neural Network based on the Levenberg-Marquardt algorithm (LMA) for the detection of human presence in a room of SHC with the use of predictive calculation of CO 2 concentrations from obtained measurements of temperature (indoor, outdoor) T i , T o and relative air humidity rH. Based on the long-term monitoring (1 month) of operational and technical functions (unregulated, uncontrolled) in an experimental Smart Home (SH), LMA was trained through the data picked up by the sensors of CO 2 , T and rH with the aim to indirectly predict CO 2 leading to the elimination of CO 2 sensor from the measurement process. Within the realized experiment, input parameters of the neuronal network and the number of neurons for LMA were optimized on the basis of calculated values of Root Mean Squared Error, the correlative coefficient (R) and the length of the measured training time ANN. With the use of the trained network ANN, we realized a strictly controlled short-term (11 h) experiment without the use of CO2 sensor. Experimental results verified high method accuracy (>95%) within the short-term and long-term experiments for learned ANN (1.6.2015ANN (1.6. -30.6.2015. For learned ANN (1.2.2014ANN (1.2. -27.2.2014) was verified worse method accuracy (>60%). The original contribution is a verification of a low-cost method for the detection of human presence in the real operating environment of SHC. In the third part of the paper is described the practical implementation of voice control of operating technical functions by the KNX technology in SHC by means of the in-house developed application HESTIA, intended for both the desktop system version and the mobile version of the Windows 10 operating system for mobile phones. The resultant application can be configured for any building equipped with the KNX bus system. Voice control implementation is an in-house solution, no third-party software is used here. Utilization of the voice communication application in SHC was proven on the experimental basis with the combination of measurement CO 2 for ADL monitoring in SHC.

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Section: First Part-use Of Co 2 Sensors For Determining the Presence mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring of the daily living activities in smart home care

Vaňuš

Belesova

Martínek

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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“…Secondly, we will look at soft computing methods and describe how they have the ability to enhance the performance of adaptive signal processing algorithms in achieving better outcomes when processing biomedical signals. Then we will pay special attention to adaptive neuro-fuzzy inference systems (ANIFS) [60,63], which are considered to be the most significant in fetal electrocardiography research.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Adaptive Signal Processing Improves the Diagnostic Quality of Transabdominal Fetal Electrocardiography

Martínek¹,

Kahánková²,

Skukova³

et al. 2016

Advanced Biosignal Processing and Diagnostic Methods

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The abdominal fetal electrocardiogram (fECG) conveys valuable information that can aid clinicians with the diagnosis and monitoring of a potentially at risk fetus during pregnancy and in childbirth. This chapter primarily focuses on noninvasive (external and indirect) transabdominal fECG monitoring. Even though it is the preferred monitoring method, unlike its classical invasive (internal and direct) counterpart (transvaginal monitoring), it may be contaminated by a variety of undesirable signals that deteriorate its quality and reduce its value in reliable detection of hypoxic conditions in the fetus. A stronger maternal electrocardiogram (the mECG signal) along with technical and biological artifacts constitutes the main interfering signal components that diminish the diagnostic quality of the transabdominal fECG recordings. Currently, transabdominal fECG monitoring relies solely on the determination of the fetus' pulse or heart rate (FHR) by detecting RR intervals and does not take into account the morphology and duration of the fECG waves (P, QRS, T), intervals, and segments, which collectively convey very useful diagnostic information in adult cardiology. The main reason for the exclusion of these valuable pieces of information in the determination of the fetus' status from clinical practice is the fact that there are no sufficiently reliable and well-proven techniques for accurate extraction of fECG signals and robust derivation of these informative features. To address this shortcoming in fetal cardiology, we focus on adaptive signal processing methods and pay particular attention to nonlinear approaches that carry great promise in improving the quality of transabdominal fECG monitoring and consequently impacting fetal cardiology in clinical practice. Our investigation and experimental results by using clinicalquality synthetic data generated by our novel fECG signal generator suggest that adaptive neuro-fuzzy inference systems could produce a significant advancement in fetal monitoring during pregnancy and childbirth. The possibility of using a single device to

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“…However, the input-output memberships are fixed and cannot be studied during the whole control process, and its parameters depend on experience. If the controller uses neural network [18][19][20], the result is based on the training algorithm. The neural network controller can learn, but the response is relatively slow; the transient time is large, and the input and output relations are very difficult to express.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Current Control Method for Hybrid Active Power Filter

Chau

2016

Journal of Electrical Engineering

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This paper proposes an adaptive current control method for Hybrid Active Power Filter (HAPF). It consists of a fuzzy-neural controller, identification and prediction model and cost function. The fuzzy-neural controller parameters are adjusted according to the cost function minimum criteria. For this reason, the proposed control method has a capability on-line control clings to variation of the load harmonic currents. Compared to the single fuzzy logic control method, the proposed control method shows the advantages of better dynamic response, compensation error in steady-state is smaller, able to online control is better and harmonics cancelling is more effective. Simulation and experimental results have demonstrated the effectiveness of the proposed control method.

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A Robust Approach For Acoustic Noise Suppression In Speech Using ANFIS

Cited by 19 publications

References 20 publications

Monitoring of the daily living activities in smart home care

Monitoring of the daily living activities in smart home care

Nonlinear Adaptive Signal Processing Improves the Diagnostic Quality of Transabdominal Fetal Electrocardiography

Adaptive Current Control Method for Hybrid Active Power Filter

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