Reduction of nonstationary acoustic noise in speech using LMS adaptive noise cancelling

Pulsipher, D. C.; Boll, S.; Rushforth, C.K.; Timothy, L.

doi:10.1109/icassp.1979.1170761

Cited by 11 publications

(4 citation statements)

References 4 publications

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“…A wavelet de-noising technique is used in (Haibin et al 2008) to eliminate heart sound interference, such as low frequency noise due to holding the stethoscope on an undesired location or internal noise in the hardware itself during recording. Müller and Nolte (2006) and Pulsipher (1979) utilized independent component analysis (ICA) and the adaptive least mean squares (LMS) method for noise cancellation and adaptive filtering, respectively. Vernekar (2016) filtered the heart sound signal using a band-pass Butterworth filter ranging from 25 Hz to 400 Hz to remove high and low frequency noise, and employed the Schmidt spike removal technique (Schmidt et al 2010) to remove signal spikes.…”

Section: Introductionmentioning

confidence: 99%

Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features

Khan

Abid²,

Khan³

2020

Physiol. Meas.

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Objective: Heart abnormality detection using heart sound signals (phonocardiogram (PCG)) has been an active research area for the last few decades. In this paper, automatic heart sound classification using segmented and unsegmented PCG signals is presented. Approach: In this paper: (i) we perform an in-depth analysis of various time and frequency domain features, followed by experimental determination of effective feature subsets for improved classification performance; (ii) both segmented and unsegmented PCG signals are studied and important results concerning the respective feature subsets and their classification performances are reported; and (iii) different classification algorithms, including the support vector machine, kth nearest neighbor, decision tree, ensemble classifier, artificial neural network and long short-term memory network (LSTMs), are employed to evaluate the performance of the proposed feature subsets and their comparison with other established features and methods is presented. Main results: It is observed that LSTM performs better on mel-frequency cepstral coefficient (MFCC) features extracted from unsegmented PCG data, with an area under curve (AUC) score of 91.39%, however, the MFCC features do not show a consistent performance with other classifiers (the second highest AUC score is 62.08% with the decision tree classifier). In contrast, in the case of time-frequency features from segmented data, the performance of all the classifiers is appreciable with AUC scores over 70%. In particular, the conventional machine learning techniques shows consistency in achieving over 80% in AUC scores. Significance: The results of this study highlight the importance of time and frequency domain features. Thus it is necessary to employ both the time and frequency features of segmented PCG signals to achieve improved classification.

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Section: Introductionmentioning

confidence: 99%

Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features

Khan

Abid²,

Khan³

2020

Physiol. Meas.

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“…The LMS algorithm, developed by Hoff in 1959 ͑Widrow et al, 1985;Haykin et al, 1986͒, has been studied in great detail and has found a myriad of applications ͑Widrow et al, 1975;Darlington et al, 1985;Rodriguez et al, 1987;Harrison et al, 1986;Pulsipher et al, 1979;Elliott et al, 1987;Poole et al, 1984͒. Figure 2͑a͒ depicts the basic construct for noise reduction in a linear system with additive noise.…”

Section: A Least-mean-squares "Lms… Algorithmmentioning

confidence: 98%

An adaptive noise reduction stethoscope for auscultation in high noise environments

Patel

Callahan²,

Callahan³

et al. 1998

The Journal of the Acoustical Society of America

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Auscultation of lung sounds in patient transport vehicles such as an ambulance or aircraft is unachievable because of high ambient noise levels. Aircraft noise levels of 90-100 dB SPL are common, while lung sounds have been measured in the 22-30 dB SPL range in free space and 65-70 dB SPL within a stethoscope coupler. Also, the bandwidth of lung sounds and vehicle noise typically has significant overlap, limiting the utility of traditional band-pass filtering. In this study, a passively shielded stethoscope coupler that contains one microphone to measure the (noise-corrupted) lung sound and another to measure the ambient noise was constructed. Lung sound measurements were made on a healthy subject in a simulated USAF C-130 aircraft environment within an acoustic chamber at noise levels ranging from 80 to 100 dB SPL. Adaptive filtering schemes using a least-mean-squares (LMS) and a normalized least-mean-squares (NLMS) approach were employed to extract the lung sounds from the noise-corrupted signal. Approximately 15 dB of noise reduction over the 100-600 Hz frequency range was achieved with the LMS algorithm, with the more complex NLMS algorithm providing faster convergence and up to 5 dB of additional noise reduction. These findings indicate that a combination of active and passive noise reduction can be used to measure lung sounds in high noise environments.

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“…For example, in biomedical signal processing, source signals are very weak, noisy, and non-stationary [1]. Further, in many other applications such as under water acoustic signal classification [2], speech signal processing [3], feature extraction for acoustic target recognition [4], and analysis of acoustic emission signals [5], the signals of interest are of highly nonstationary nature. Lanka (e-mail: nimesh@ee.pdn.ac.lk, wijayanthanet@ee.pdn.ac.lk,amanthi@ee.pdn.ac.lk,roshangodd@ee.pdn.ac.lk, mpb.ekanayake@ee.pdn.ac.lk, and jan@ee.pdn.ac.lk).…”

Section: Introductionmentioning

confidence: 99%

Extraction and classification of non-stationary acoustic signals via dynamic subspace filtering

Pollwaththage

Nettasinghe

Ratnayake

et al. 2013

2013 IEEE 8th International Conference on Industrial and Information Systems

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This paper presents a dynamic, subspace based approach for extraction and classification of non-stationary acoustic signals under noisy conditions. The stationary subspace methods commonly used for noise removal take the whole signal into consideration while determining the signal subspace. This, for a non-stationary signal implies that spectral variations that occur through time are not taken into account. Thus, the overall signal subspace formulated will correspond to noise subspaces at certain points of time due to the highly non-stationary nature of the signal, resulting in noise leakage. The method proposed in this paper performs a subspace analysis dynamically at various stages of the observation period, enabling the signal subspace to evolve according to the non-stationary nature of the signal to be extracted. For signal classification problems, this enables the classifier to latch onto details about the non-stationarity in the signal which in turn provides valuable information with regard to the uniqueness of each signal class. Finally, an analysis is conducted on the resultant signals to prove the viability of the proposed methodology for signal extraction and classification problems under heavy noise conditions where the signals in concern are highly non-stationary.

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Reduction of nonstationary acoustic noise in speech using LMS adaptive noise cancelling

Cited by 11 publications

References 4 publications

Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features

Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features

An adaptive noise reduction stethoscope for auscultation in high noise environments

Extraction and classification of non-stationary acoustic signals via dynamic subspace filtering

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