• ETRI Journal
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• v.33 no.6
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• pp.871-879
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• 2011

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.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2228-2231
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• 2003

In this paper, a transcoding algorithm for the Selectable Mode Vocoder (SMV) and the G.723.1 speech coder via direct parameter transformation is proposed. In contrast to the conventional tandem transcoding algorithm, the proposed algorithm converts the parameters of one coder to the Other Without going through the decoding md encoding process. The proposed algorithm is composed of four parts: the parameter decoding, line spectral pair (LSP) conversion, pitch period conversion, excitation conversion and rate selection. The evaluation results show that the proposed algorithm achieves equivalent speech quality to that of tandem transcoding with reduced computational complexity and delay.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.61-70
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• 2003

In this paper, a transcoding algorithm for the Selectable Mode Vocoder (SMV) and the G.723.1 speech coder via direct parameter transformation is proposed. In contrast to the conventional tandem transcoding algorithm, the proposed algorithm converts the parameters of one coder to the other without going through the decoding and encoding process. The proposed algorithm is composed of four parts: the parameter decoding, line spectral pair (LSP) conversion, pitch period conversion, excitation conversion and rate selection. The evaluation results show that the proposed algorithm achieves equivalent speech quality to that of tandem transcoding with reduced computational complexity and delay.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.71-83
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• 2003

In this paper, a novel transcoding algorithm for the G.729A and the Selectable Mode Vocoder(SMV) vocoders via direct parameter transformation is proposed. In contrast to the conventional tandem transcoding algorithm, the proposed algorithm converts the parameters of one coder to the other without going through the decoding and encoding processes. In transcoder from SMV to G.729A, LSP conversion algorithm, pitch delay conversion algorithm and transcoding algorithm in lower rate are proposed, and in transcoder from G.729A to SMV, LSP conversion algorithm, pitch delay conversion algorithm and rate selection algorithm are proposed. Evaluation results show that while exhibiting better computational and delay characteristics, the proposed algorithm produces equivalent or Improved speech quality to that produced by the tandem transcoding algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.149-156
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• 2012

Support vector machines (SVMs) are well known for their pattern recognition capability, but proper care should be taken to alleviate their inherent implementation cost resulting from high computational intensity and memory requirement, especially in embedded systems where only limited resources are available. Since the memory requirement determined by the dimensionality and the number of support vectors is generally too high for a cache in embedded systems to accomodate, frequent accesses to the main memory occur inevitably whenever the cache is not able to provide requested data to the processor. These frequent accesses to the main memory result in overall performance degradation and increased energy consumption because a memory access typically takes longer and consumes more energy than a cache access or a register access. In this paper, we propose a technique that reduces the number of main memory accesses by optimizing the data access pattern of the SVM-based classifier in such a way that the temporal locality of the accesses increases, fully utilizing data loaded into the processor chip. With experiments, we confirm the enhancement made by the proposed technique in terms of the number of memory accesses, overall execution time, and energy consumption.