Noise robust zerocrossing rate computation for audio signal classification

Kathirvel, P.; Manikandan, M. Sabarimalai; Senthilkumar, Subikshaa; Soman, K P

doi:10.1109/tisc.2011.6169086

Cited by 15 publications

(5 citation statements)

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“…where N denotes the number of samples [27]. In this work, the ZCR value obtained for a 3 s ECG signal is used to discriminate the VTVF episodes from the non-VTVF episodes.…”

Section: Proposed Vt/vf Detection Methodmentioning

confidence: 99%

“…The feature signal z [ n ] is computed as the additive mixture of the filtered ECG signal s [ n ] and the bipolar sequence w [ n ] with length of N samples that has higher zero‐crossings of N − 1. The feature signal z [ n ] is obtained as

z false[n false] = s false[n false] + w false[n false] .

For the feature signal z [ n ], the ZCR is computed as

normalZCR = \frac{1}{N} {false\sum}_{n = 0}^{N} | sgn (z [n]) - sgn (z [n - 1]) |,

where N denotes the number of samples [27]. In this work, the ZCR value obtained for a 3 s ECG signal is used to discriminate the VTVF episodes from the non‐VTVF episodes.…”

Section: Proposed Vt/vf Detection Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Efficient and robust ventricular tachycardia and fibrillation detection method for wearable cardiac health monitoring devices

Prabhakararao

Manikandan

2016

Healthcare Technology Letters

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In this Letter, the authors propose an efficient and robust method for automatically determining the VT and VF events in the electrocardiogram (ECG) signal. The proposed method consists of: (i) discrete cosine transform (DCT)-based noise suppression; (ii) addition of bipolar sequence of amplitudes with alternating polarity; (iii) zero-crossing rate (ZCR) estimation-based VTVF detection; and (iv) peak-to-peak interval (PPI) feature based VT/VF discrimination. The proposed method is evaluated using 18,000 episodes of different ECG arrhythmias taken from 6 PhysioNet databases. The method achieves an average sensitivity (Se) of 99.61%, specificity (Sp) of 99.96%, and overall accuracy (OA) of 99.92% in detecting VTVF and non-VTVF episodes by using a ZCR feature. Results show that the method achieves a Se of 100%, Sp of 99.70% and OA of 99.85% for discriminating VT from VF episodes using PPI features extracted from the processed signal. The robustness of the method is tested using different kinds of ECG beats and various types of noises including the baseline wanders, powerline interference and muscle artefacts. Results demonstrate that the proposed method with the ZCR, PPI features can achieve significantly better detection rates as compared with the existing methods.

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“…where N denotes the number of samples [27]. In this work, the ZCR value obtained for a 3 s ECG signal is used to discriminate the VTVF episodes from the non-VTVF episodes.…”

Section: Proposed Vt/vf Detection Methodmentioning

confidence: 99%

z false[n false] = s false[n false] + w false[n false] .

For the feature signal z [ n ], the ZCR is computed as

normalZCR = \frac{1}{N} {false\sum}_{n = 0}^{N} | sgn (z [n]) - sgn (z [n - 1]) |,

where N denotes the number of samples [27]. In this work, the ZCR value obtained for a 3 s ECG signal is used to discriminate the VTVF episodes from the non‐VTVF episodes.…”

Section: Proposed Vt/vf Detection Methodsmentioning

confidence: 99%

Efficient and robust ventricular tachycardia and fibrillation detection method for wearable cardiac health monitoring devices

Prabhakararao

Manikandan

2016

Healthcare Technology Letters

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“…In this paper [15]it has been stated that zero-rate (ZCR) is one of the most important acoustic features widely used in voice activity detection, voiced/unvoiced speech classification, image processing for speech classification, optics, biomedical engineering, radar and fluid mechanics. Naive Bayes is a collection of controlled strategies in machine learning, used for classification.…”

Section: Related Workmentioning

confidence: 99%

Predicting Effectiveness of Marketing through Analyzing Emotional Context in Advertisement using Deep Learning

Arafat¹,

Islam²,

Rafi³

et al. 2021

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)

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In this modern age, marketing strategy is becoming a new challenge. Not only the global market but also people's choices are shifting to catch the attention of buyers. Also, based on consumer's choice organizations are bringing changes in their marketing policy to increase the chances of their product selling rate. Basically, to promote their products and grab buyer's attention they are promoting advertisements on every media platform. But they are not aware of the effectiveness of marketing and which emotional states are needed more and which are not needed much. Therefore, we lead this study to recognize a successful advertisement and identify the rate of the emotional states which make good impact in people mind to purchase the product. Using deep learning and supervised machine learning algorithms as well as feature extraction methods for instance, LSTM-RNN, SVM, XGBOOST, Naïve Bayes, Multiple Linear Regression, MFCC, Zero-Crossing Rate, Power Spectral Density, we find out and evaluate the rate of the emotional states to figure out the liking and purchase intent which makes an advertisement successful.

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“…A voice activity detector basically consists of two main processes, namely feature extraction and classification. Some of the popular features in speech processing are zero crossing rate [4], energy [5], signal-to-noise ratio, spectral flatness [6], correlation [7], etc. Instead of modeling the dynamic noise features using support vector machine (SVM) trained on noise-labeled training data [8], some recent VADs focus on the extraction of robust speech features such as the formant frequencies of eight English vowels [9].…”

Section: Discriminative Features and Classificationmentioning

confidence: 99%

Real-Time Robust Voice Activity Detection Using the Upper Envelope Weighted Entropy Measure and the Dual-Rate Adaptive Nonlinear Filter

Ong

Tan

Vengadasalam

et al. 2017

Entropy

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Voice activity detection (VAD) is a vital process in voice communication systems to avoid unnecessary coding and transmission of noise. Most of the existing VAD algorithms continue to suffer high false alarm rates and low sensitivity when the signal-to-noise ratio (SNR) is low, at 0 dB and below. Others are developed to operate in offline mode or are impractical for implementation in actual devices due to high computational complexity. This paper proposes the upper envelope weighted entropy (UEWE) measure as a means to enable high separation of speech and non-speech segments in voice communication. The asymmetric nonlinear filter (ANF) is employed in UEWE to extract the adaptive weight factor that is subsequently used to compensate the noise effect. In addition, this paper also introduces a dual-rate adaptive nonlinear filter (DANF) with high adaptivity to rapid time-varying noise for computation of the decision threshold. Performance comparison with standard and recent VADs shows that the proposed algorithm is superior especially in real-time practical applications.

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Noise robust zerocrossing rate computation for audio signal classification

Cited by 15 publications

References 15 publications

Efficient and robust ventricular tachycardia and fibrillation detection method for wearable cardiac health monitoring devices

Efficient and robust ventricular tachycardia and fibrillation detection method for wearable cardiac health monitoring devices

Predicting Effectiveness of Marketing through Analyzing Emotional Context in Advertisement using Deep Learning

Real-Time Robust Voice Activity Detection Using the Upper Envelope Weighted Entropy Measure and the Dual-Rate Adaptive Nonlinear Filter

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