Multiclass support vector machines for environmental sounds classification in visual domain based on log-Gabor filters

Souli, Sameh; Lachiri, Zied

doi:10.1007/s10772-012-9174-0

Cited by 16 publications

(5 citation statements)

References 15 publications

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“…Such Gabor functions have been previously used for analyzing auditory signals by several authors, including (Wolfe et al 2001, Kleinschmidt 2002, Kleinschmidt & Gelbart 2002, Lobo & Loizou 2003, Qiu et al 2003, van de Boogart & Lienhart 2006, Ezzat et al 2007, Domont et al 2008, He et al 2009, Heckmann et al 2011, Wu et al 2011, Schädler et al 2012, Sameh & Lachiri 2013. Our theory provides a new way of deriving this form of representation with special emphasis on the multi-scale nature of the Gaussian window functions and their resulting cascade properties between spectrograms at different temporal scales.…”

Section: Time-causal Temporal Scale-spacementioning

confidence: 98%

“…By rewriting the expression ( 54 ) for the complex-valued spectrogram based on the Gaussian temporal scale-space concept as it can be seen that up to a phase shift, this multi-scale spectrogram can equivalently be interpreted as the convolution of the original auditory signal f by Gabor functions [ 28 ] of the form Such Gabor functions have been previously used for analyzing auditory signals by several authors, including (Wolfe et al [ 29 ]; Kleinschmidt et al [ 49 , 50 ]; Lobo and Loizou [ 30 ]; Qiu et al [ 31 ]; van de Boogart and Lienhart [ 51 ]; Ezzat et al [ 52 ]; Domont et al [ 53 ]; He et al [ 54 ]; Heckmann et al [ 55 ]; Wu et al [ 32 ]; Schädler et al [ 56 ]; Sameh and Lachiri [ 57 ]). Our theory provides a new way of deriving this representation with special emphasis on the multi-scale nature of the Gaussian window functions and their resulting cascade properties between spectrograms at different temporal scales.…”

Section: Multi-scale Spectrograms For Auditory Signalsmentioning

confidence: 99%

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Idealized Computational Models for Auditory Receptive Fields

Lindeberg

Friberg²

2015

PLoS ONE

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We present a theory by which idealized models of auditory receptive fields can be derived in a principled axiomatic manner, from a set of structural properties to (i) enable invariance of receptive field responses under natural sound transformations and (ii) ensure internal consistency between spectro-temporal receptive fields at different temporal and spectral scales. For defining a time-frequency transformation of a purely temporal sound signal, it is shown that the framework allows for a new way of deriving the Gabor and Gammatone filters as well as a novel family of generalized Gammatone filters, with additional degrees of freedom to obtain different trade-offs between the spectral selectivity and the temporal delay of time-causal temporal window functions. When applied to the definition of a second-layer of receptive fields from a spectrogram, it is shown that the framework leads to two canonical families of spectro-temporal receptive fields, in terms of spectro-temporal derivatives of either spectro-temporal Gaussian kernels for non-causal time or a cascade of time-causal first-order integrators over the temporal domain and a Gaussian filter over the logspectral domain. For each filter family, the spectro-temporal receptive fields can be either separable over the time-frequency domain or be adapted to local glissando transformations that represent variations in logarithmic frequencies over time. Within each domain of either non-causal or time-causal time, these receptive field families are derived by uniqueness from the assumptions. It is demonstrated how the presented framework allows for computation of basic auditory features for audio processing and that it leads to predictions about auditory receptive fields with good qualitative similarity to biological receptive fields measured in the inferior colliculus (ICC) and primary auditory cortex (A1) of mammals.

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Section: Time-causal Temporal Scale-spacementioning

confidence: 98%

Section: Multi-scale Spectrograms For Auditory Signalsmentioning

confidence: 99%

Idealized Computational Models for Auditory Receptive Fields

Lindeberg

Friberg²

2015

PLoS ONE

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“…In identifying seizure activities, the shape and direction of energy distribution in time-frequency signals are important; these features can be extracted using directional or wavelet decomposition filters and the features can be captured in a number of images. The images can later be used to obtain statistical features [14]. There are other methods that can be used for the same extraction purpose, such as dimensionality reduction methods proposed by Sameh and Lachiri [14].…”

Section: Introductionmentioning

confidence: 99%

“…The images can later be used to obtain statistical features [14]. There are other methods that can be used for the same extraction purpose, such as dimensionality reduction methods proposed by Sameh and Lachiri [14].…”

Section: Introductionmentioning

confidence: 99%

The State of the Art in Feature Extraction Methods for EEG Classification

Mohammad

Hussen

2019

UHD J SCI TECH

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Epileptic seizure is a neurological disease that is common around the world and there are many types (e.g. Focal aware seizures and atonic seizure) that are caused by synchronous or abnormal neuronal activity in the brain. A number of techniques are available to detect the brain activities that lead to Epileptic seizures; one of the most common one is Electroencephalogram (EEG) that uses visual scanning to measure brain activities generated by nerve cells in the cerebral cortex. The techniques make use of different features detected by EEG to decide on the occurrence and type of seizures. In this paper we review EEG features proposed by different researches for the purpose of Epileptic seizure detection, also analyze, and compare the performance of the proposed features.

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“…Typical t-f signal classification approaches can be classified into two categories: 1) template matching approaches, where a t-f representation of a given signal is correlated with stored templates to detect the presence of abnormality [7]; 2) machine learning approaches that involve such steps as feature extraction and classifier training [2]. The timefrequency features can be extracted by dimensionality reduction approaches such as singular value decomposition [2], interpreting TFDs as images and using texture information as features [8,9], separating signal components using empirical mode decomposition and extracting features from the separated components [3,5,6].…”

Section: Introductionmentioning

confidence: 99%

Classification of EEG Signals Using Adaptive Time-Frequency Distributions

Khan¹,

Ali²

2016

Metrology and Measurement Systems

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Time-Frequency (t-f) distributions are frequently employed for analysis of new-born EEG signals because of their non-stationary characteristics. Most of the existing time-frequency distributions fail to concentrate energy for a multicomponent signal having multiple directions of energy distribution in the t-f domain. In order to analyse such signals, we propose an Adaptive Directional Time-Frequency Distribution (ADTFD). The ADTFD outperforms other adaptive kernel and fixed kernel TFDs in terms of its ability to achieve high resolution for EEG seizure signals. It is also shown that the ADTFD can be used to define new time-frequency features that can lead to better classification of EEG signals, e.g. the use of the ADTFD leads to 97.5% total accuracy, which is by 2% more than the results achieved by the other methods.

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Multiclass support vector machines for environmental sounds classification in visual domain based on log-Gabor filters

Cited by 16 publications

References 15 publications

Idealized Computational Models for Auditory Receptive Fields

Idealized Computational Models for Auditory Receptive Fields

The State of the Art in Feature Extraction Methods for EEG Classification

Classification of EEG Signals Using Adaptive Time-Frequency Distributions

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