Robust feature extraction using subband spectral centroid histograms

Gajić, Bojana; Paliwal, K.K.

doi:10.1109/icassp.2001.940773

Cited by 21 publications

(12 citation statements)

References 7 publications

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“…In the case of MFCCs, which are the most commonly used parameterization method for the detection of murmur episodes, the number of contours used in the literature ranges from 12 to 36 coefficients. 22,33,53,63,67 Meanwhile, the results demonstrated that the joint information for a large number of MFCC time-varying contours tends to be reduced (see Fig. 4a), and the correct rate of classification begins to decay when more than 11 dynamic features were taken into account (Fig.…”

Section: Discussionmentioning

confidence: 88%

“…An effective method to combine the frequency and magnitude information from the power spectrum can be achieved through computation of subband spectral centroid histograms. 22 Therefore, the time-varying estimate for the surrounding energy of the centroid, within a fixed bandwidth, becomes an stochastic contour that can be computed. A centroid is calculated for each filter in the frequency domain, H 0 m ½k, by:…”

Section: Filter-bank Based Tfr Contour Featuresmentioning

confidence: 99%

“…By setting c < 1, the dynamic range of the power spectrum can be reduced. 22 The energy around each centroid for a fixed bandwidth Dk is computed by means of:…”

Section: Filter-bank Based Tfr Contour Featuresmentioning

confidence: 99%

“…Treating the data as stochastically dependent, for which natural ordering is important, provides a possible approach, capable of capturing the evolving information of the structure. In this sense, a large amount of features have been proposed before in the existing literature: instantaneous energy, 1,36,37 instantaneous frequency, 50,53,67 instantaneous equivalent bandwidth, 66 eigenvectors, 24 subband spectral centroid histograms, 22 and Mel frequency cepstral coefficients (MFCC). 6,40,41,47,63,64 Mainly, these dynamic features are typically estimated based on STFT, which is not capable of revealing the t-f dynamics of murmurs.…”

Section: Introductionmentioning

confidence: 99%

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Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals

et al. 2009

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This work discusses a method for the selection of dynamic features, based on the calculation of the spectral power through time applied to the detection of systolic murmurs from phonocardiographic recordings. To investigate the dynamic properties of the spectral power during murmurs, several quadratic energy distributions have been studied, namely Wigner-Ville, Choi-Williams, smoothed pseudo Wigner-Ville, exponential, and hyperbolic T-distribution. The classification performance has been compared with that using a Short Time Fourier Transform and Continuous Wavelet Transform representations. Furthermore, this work discusses a variety of nonparametric techniques to estimate the spectral power contours as dynamic features that characterize the heart sounds (HS): instantaneous energy, eigenvectors, instantaneous frequency, equivalent bandwidth, subband spectral centroids, and Mel cepstral coefficients. In this way, the aforementioned time-frequency representations and their dynamic features were evaluated by means of their ability to detect the presence of murmurs using a simple k-Nearest Neighbors classifier. Moreover, the relevancies of the proposed dynamic features have been evaluated using a time-varying principal component analysis. The work presented is carried out using a database containing 22 phonocardiographic recordings (16 normal and 6 records with murmurs), segmented to extract 402 representative individual beats (201 per class). The results suggest that the smoothing given by the quadratic energy distribution significantly improves the classification performance for the detection of murmurs in HS. Moreover, it is shown that the power dynamic features which give the best overall classification performance are the MFCC contours. As a result, the proposed method can be implemented as a simple diagnostic tool for primary health-care purposes with high accuracy (up to 98%) discriminating between normal and pathologic beats.

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Section: Discussionmentioning

confidence: 88%

Section: Filter-bank Based Tfr Contour Featuresmentioning

confidence: 99%

“…By setting c < 1, the dynamic range of the power spectrum can be reduced. 22 The energy around each centroid for a fixed bandwidth Dk is computed by means of:…”

Section: Filter-bank Based Tfr Contour Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals

et al. 2009

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“…Perceptually, it has a robust connection with the impression of "brightness" of a sound. It is calculated as the weighted mean of the frequencies present in the signal, determined using a Fourier transform, with their magnitudes as the weights [19].…”

Section: C) Perceptual Featuresmentioning

confidence: 99%

Audio Music Monitoring: Analyzing Current Techniques for Song Recognition and Identification

Senevirathna¹,

Jayaratne²

2015

GSTF J Comput

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Abstract-when people are attaching or interesting in something, usually they are trying to interact with it frequently. Music is attached to people since the day of they were born. When music repository grows, people faced lots of challenges such as finding a song quickly, categorizing, organizing and even listening again when they want etc. Because of this, people tend to find electronic solutions. To index music, most of the researchers use content based information retrieval mechanism since content based classification doesn't need any additional information rather than audio features embedded to it. As well as it is the most suitable way to search music, when user don't know the meta data attached to it, like author of the song. The most valuable application of this audio recognition is copyright infringement detection. Throughout this survey we will present approaches which were proposed by various researchers to detect, recognize music using content base mechanisms. And finally we will conclude this by analyzing the current status of this era.

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Multimedia data mining: state of the art and challenges

Bhatt

Kankanhalli

2010

Multimed Tools Appl

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Robust feature extraction using subband spectral centroid histograms

Cited by 21 publications

References 7 publications

Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals

Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals

Audio Music Monitoring: Analyzing Current Techniques for Song Recognition and Identification

Multimedia data mining: state of the art and challenges

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