SMVLite: Reduced Complexity Selectable Mode Vocoder

Goudar, C.V.; Rabha, P.; Deshpande, Murali Mohan; Rao, A. C.

doi:10.1109/icassp.2006.1660117

Cited by 5 publications

(4 citation statements)

References 3 publications

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“…The SMV is an adaptive multi-rate speech codec capable of efficiently utilising limited bandwidth and has been adopted as the standard for 3GPP2 [8,9]. It features four average data rates and four operational modes, each dynamically chosen based on the types of input frames and the status of communication channels, respectively.…”

Section: Brief Review Of Smv Codec and Svmmentioning

confidence: 99%

Enhancing support vector machine-based speech/music classification using conditional maximum a posteriori criterion

Lim

Chang

2012

IET Signal Process.

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Support vector machines (SVMs) have been recognised as a promising technique in the field of pattern recognition, and one of the interesting applications of this technique is speech/music classification. In this study, the authors propose a novel approach to improve the SVM-based speech/music classification using the second-order conditional maximum a posteriori (CMAP). To do this, the authors first devise a method to estimate a posteriori probability to select between speech and music from the SVM output. This is achieved by employing the sigmoid function, obtained by optimised data training. A final speech/music classification is then acquired using the second-order CMAP with a maximum a posteriori probability depending not only on the current observation, but also on the classification results of two previous frames, incorporating substantial inter-frame correlations. While conventional SVM optimisation techniques are used during the training phase, the proposed technique can be inherently adopted in the classification phase. In this regard, the proposed approach can be developed and employed in parallel with other optimisation techniques. Experimental results show that the proposed algorithm yields better results than the speech/music classification rule in SVM.

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Section: Brief Review Of Smv Codec and Svmmentioning

confidence: 99%

Enhancing support vector machine-based speech/music classification using conditional maximum a posteriori criterion

Lim

Chang

2012

IET Signal Process.

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“…The SMV is a low bit-rate speech codec in the 3GPP2 that is adopted in the CDMA2000 network [6]. The SMV provides four bit-rates: Rate 1 (8.55 kbps), Rate 1/2 (4.0 kbps), Rate 1/4 (2.0 kbps), and Rate 1/8 (0.8 kbps), and modes : Mode 0 (premium), Mode 1 (standard), Mode 2 (economy), and Mode 3 (supereconomy) in which different average bit-rates (ABR) can be selected according to the system capacity and the speech quality.…”

Section: The 3gpp2 Selectable Mode Vocodermentioning

confidence: 99%

Analysis and Improvement of Speech/Music Classification for 3GPP2 SMV Based on GMM

Song

Lee

Chang

et al. 2008

IEEE Signal Process. Lett.

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In this letter, a novel approach is proposed to improve the performance of speech/music classification for the selectable mode vocoder (SMV) of 3GPP2 using the Gaussian mixture model (GMM). An in-depth analysis of the features and classification method adopted in the conventional SMV is performed. Feature vectors applied to the GMM are then selected from the relevant parameters of the SMV for efficient speech/music classification. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme implemented in the SMV.Index Terms-Gaussian mixture model (GMM), selectable mode vocoder (SMV), speech/music classification.

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“…SMV is an adaptive multirate speech codec adopted as a standard in 3GPP2 and known for its high efficiency in utilizing limited bandwidth [12], [13]. It supports four different average data rates dynamically adjusted according to the types of input frames and four different operational modes dynamically selected based on the status of communication channels between communication stations.…”

Section: Brief Review Of Smv Codecmentioning

confidence: 99%

Adaptive Kernel Function of SVM for Improving Speech/Music Classification of 3GPP2 SMV

Lim¹,

Chang²

2011

ETRI J

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Because a wide variety of multimedia services are provided through personal wireless communication devices, the demand for efficient bandwidth utilization becomes stronger. This demand naturally results in the introduction of the variable bitrate speech coding concept. One exemplary work is the selectable mode vocoder (SMV) that supports speech/music classification. However, because it has severe limitations in its classification performance, a couple of works to improve speech/music classification by introducing support vector machines (SVMs) have been proposed. While these approaches significantly improved classification accuracy, they did not consider correlations commonly found in speech and music frames. In this paper, we propose a novel and orthogonal approach to improve the speech/music classification of SMV codec by adaptively tuning SVMs based on interframe correlations. According to the experimental results, the proposed algorithm yields improved results in classifying speech and music within the SMV framework.

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SMVLite: Reduced Complexity Selectable Mode Vocoder

Cited by 5 publications

References 3 publications

Enhancing support vector machine-based speech/music classification using conditional maximum a posteriori criterion

Enhancing support vector machine-based speech/music classification using conditional maximum a posteriori criterion

Analysis and Improvement of Speech/Music Classification for 3GPP2 SMV Based on GMM

Adaptive Kernel Function of SVM for Improving Speech/Music Classification of 3GPP2 SMV

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