Classification of audio signals using SVM and RBFNN

Dhanalakshmi, P.; Palanivel, S.; Ramalingam, V.

doi:10.1016/j.eswa.2008.06.126

Cited by 147 publications

(68 citation statements)

References 16 publications

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“…After performing PCA, the hidden layer neurons of the RBFNs have been modeled by considering intra-class discriminating characteristics of the training images. Dhanalaksmi et al Dhanalaksmi et al [175], have used SVM and RBFNs algorithms to automatically classify audio clips into one of the six classes: news, sports, music, movies, advertisement, and cartoon. For these categories a number of acoustic features such as linear predictive coe cient, mel-frequency cepstral coe cients are extracted to characterize the audio content.…”

Section: Rbfns In Classi Cation and Predictionmentioning

confidence: 99%

Radial basis function neural networks: a topical state-of-the-art survey

Dash¹,

Behera²,

Dehuri

et al. 2016

Open Computer Science

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Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains. They have potential for hybridization and demonstrate some interesting emergent behaviors. This paper aims to o er a compendious and sensible survey on RBF networks. The advantages they o er, such as fast training and global approximation capability with local responses, are attracting many researchers to use them in diversi ed elds. The overall algorithmic development of RBF networks by giving special focus on their learning methods, novel kernels, and ne tuning of kernel parameters have been discussed. In addition, we have considered the recent research work on optimization of multi-criterions in RBF networks and a range of indicative application areas along with some open source RBFN tools.

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Section: Rbfns In Classi Cation and Predictionmentioning

confidence: 99%

Radial basis function neural networks: a topical state-of-the-art survey

Dash¹,

Behera²,

Dehuri

et al. 2016

Open Computer Science

View full text Add to dashboard Cite

show abstract

“…In our work experimentation was done with all the three kernels and polynomial kernel of degree 3 performed good. [23,24] The three kernel functions are given as follows:…”

Section: Figure 1 Calculation Of Lpc-mfccsmentioning

confidence: 99%

A hybrid model for neurological disordered voice classification using time and frequency domain features

K¹,

Holi

2015

AIR

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Different neurological disorders may lead to speech related problems, due to paralysis in vocal fold or weakness of the related muscles. This may modify the acoustic characteristics of the subject's voice which may provide important information for detecting certain neurological diseases. The vowel phonation which is acoustically informative and uttered by the patient with not much difficulty is collected and various acoustic features are extracted by time domain and frequency domain techniques. The use of all these features for classification may lead to a large feature space, which may lead to complexity. Hence to avoid this, in the present work experimentation is done by fusing different classifiers which are fed with features extracted from different domains. The time domain and frequency domain features are given to Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) respectively, and the intermediate decision of these classifiers is given to another SVM to identify the voice signal as normal or diseased. It is observed that this hybrid classifier model has shown some improvement with a classification accuracy of 91.43% compared to single GMM classifier with an accuracy of classification of 90% with frequency domain features as input.

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“…To classify speech/music element in audio data stream plays an important role in automatic audio classification. The method described in [1] uses SVM and Mel frequency cepstral coefficients, to accomplish multi group audio classification and categorization. The method gives in [11] uses audio classification algorithm that is based on conventional and widely accepted approach namely signal parameters by MFCC followed by GMM classification.…”

Section: Related Work 31 Audio Classificationmentioning

confidence: 99%

Audio-Video based Classification using SVM and AANN

Subashini¹,

Palanivel²,

Ramalingam³

2012

IJCA

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This paper presents a method to classify audio-video data into one of five classes: advertisement, cartoon, news, movie and songs. Automatic audio-video classification is very useful to audio-video indexing, content based audio-video retrieval. Mel frequency cepstral coefficients are used to characterize the audio data. The color histogram features extracted from the images in the video clips are used as visual features. The experiments on different genres illustrate the results of classification are significant and effective. Experimental results of audio classification and video classification are combined using weighted sum rule for audio-video based classification. The method SVM and AANN classifies the audio-video clips with an accuracy of 95.54%., and 92.94%.

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Classification of audio signals using SVM and RBFNN

Cited by 147 publications

References 16 publications

Radial basis function neural networks: a topical state-of-the-art survey

Radial basis function neural networks: a topical state-of-the-art survey

A hybrid model for neurological disordered voice classification using time and frequency domain features

Audio-Video based Classification using SVM and AANN

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