• Title/Summary/Keyword: Selectable Mode Vocoder (SMV)

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Voice-Pishing Detection Algorithm Based on 3GPP2 SMV (3GPP2 SMV 기반의 보이스 피싱 검출 알고리즘)

  • Lee, Kye-Hwan;Chang, Joon-Hyuk
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.45 no.4
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    • pp.92-99
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    • 2008
  • We propose an effective voice-pishing detection algorithm based on the 3GPP2 selectable mode vocoder (SMV). The detection of voice pishing is performed based on a Gaussian mixture model (GMM) using decoding parameters of the SMV directly extracted from the decoding process of the transmitted speech information in the mobile phone. The experimental results indicate that SMV decoding parameters are effective in discriminating between general voice and phisher's voice and the performance is significantly acceptable when the proposed technique is applied.

Enhancement Voiced/Unvoiced Sounds Classification for 3GPP2 SMV Employing GMM (3GPP2 SMV의 실시간 유/무성음 분류 성능 향상을 위한 Gaussian Mixture Model 기반 연구)

  • Song, Ji-Hyun;Chang, Joon-Hyuk
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.45 no.5
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    • pp.111-117
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    • 2008
  • In this paper, we propose an approach to improve the performance of voiced/unvoiced (V/UV) decision under background noise environments for the selectable mode vocoder (SMV) of 3GPP2. We first present an effective analysis of the features and the classification method adopted in the SMV. And then feature vectors which are applied to the GMM are selected from relevant parameters of the SMV for the efficient voiced/unvoiced classification. For the purpose of evaluating the performance of the proposed algorithm, different experiments were carried out under various noise environments and yields better results compared with the conventional scheme of the SMV.

Analysis and Implementation of Speech/Music Classification for 3GPP2 SMV Codec Employing SVM Based on Discriminative Weight Training (SMV코덱의 음성/음악 분류 성능 향상을 위한 최적화된 가중치를 적용한 입력벡터 기반의 SVM 구현)

  • Kim, Sang-Kyun;Chang, Joon-Hyuk;Cho, Ki-Ho;Kim, Nam-Soo
    • The Journal of the Acoustical Society of Korea
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    • v.28 no.5
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    • pp.471-476
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    • 2009
  • In this paper, we apply a discriminative weight training to a support vector machine (SVM) based speech/music classification for the selectable mode vocoder (SMV) of 3GPP2. In our approach, the speech/music decision rule is expressed as the SVM discriminant function by incorporating optimally weighted features of the SMV based on a minimum classification error (MCE) method which is different from the previous work in that different weights are assigned to each the feature of SMV. The performance of the proposed approach is evaluated under various conditions and yields better results compared with the conventional scheme in the SVM.

Analysis and Implementation of Speech/Music Classification for 3GPP2 SMV Codec Based on Support Vector Machine (SMV코덱의 음성/음악 분류 성능 향상을 위한 Support Vector Machine의 적용)

  • Kim, Sang-Kyun;Chang, Joon-Hyuk
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.45 no.6
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    • pp.142-147
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    • 2008
  • In this paper, we propose a novel a roach to improve the performance of speech/music classification for the selectable mode vocoder (SMV) of 3GPP2 using the support vector machine (SVM). The SVM makes it possible to build on an optimal hyperplane that is separated without the error where the distance between the closest vectors and the hyperplane is maximal. We first present an effective analysis of the features and the classification method adopted in the conventional SMV. And then feature vectors which are a lied to the SVM are selected from relevant parameters of the SMV for the efficient speech/music classification. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme of the SMV.

Analysis and Implementation of Speech/Music Classification for 3GPP2 SMV Based on GMM (3GPP2 SMV의 실시간 음성/음악 분류 성능 향상을 위한 Gaussian Mixture Model의 적용)

  • Song, Ji-Hyun;Lee, Kye-Hwan;Chang, Joon-Hyuk
    • The Journal of the Acoustical Society of Korea
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    • v.26 no.8
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    • pp.390-396
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    • 2007
  • In this letter, we propose a novel approach to improve the performance of speech/music classification for the selectable mode vocoder(SMV) of 3GPP2 using the Gaussian mixture model(GMM) which is based on the expectation-maximization(EM) algorithm. We first present an effective analysis of the features and the classification method adopted in the conventional SMV. And then feature vectors which are applied to the GMM are selected from relevant Parameters of the SMV for the efficient speech/music classification. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme of the SMV.

Enhancement of Speech/Music Classification for 3GPP2 SMV Codec Employing Discriminative Weight Training (변별적 가중치 학습을 이용한 3GPP2 SVM의 실시간 음성/음악 분류 성능 향상)

  • Kang, Sang-Ick;Chang, Joon-Hyuk;Lee, Seong-Ro
    • The Journal of the Acoustical Society of Korea
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    • v.27 no.6
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    • pp.319-324
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    • 2008
  • In this paper, we propose a novel approach to improve the performance of speech/music classification for the selectable mode vocoder (SMV) of 3GPP2 using the discriminative weight training which is based on the minimum classification error (MCE) algorithm. We first present an effective analysis of the features and the classification method adopted in the conventional SMV. And then proposed the speech/music decision rule is expressed as the geometric mean of optimally weighted features which are selected from the SMV. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme of the SMV.

Voice-Pishing Detection Algorithm Based on Minimum Classification Error Technique (최소 분류 오차 기법을 이용한 보이스 피싱 검출 알고리즘)

  • Lee, Kye-Hwan;Chang, Joon-Hyuk
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.46 no.3
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    • pp.138-142
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    • 2009
  • We propose an effective voice-phishing detection algorithm based on discriminative weight training. The detection of voice phishing is performed based on a Gaussian mixture model (GMM) incorporaiting minimum classification error (MCE) technique. Actually, the MCE technique is based on log-likelihood from the decoding parameter of the SMV(Selectable Mode Vocoder) directly extracted from the decoding process in the mobile phone. According to the experimental result, the proposed approach is found to be effective for the voice phishing detection.

Implementation of the Timbre-based Emotion Recognition Algorithm for a Healthcare Robot Application (헬스케어 로봇으로의 응용을 위한 음색기반의 감정인식 알고리즘 구현)

  • Kong, Jung-Shik;Kwon, Oh-Sang;Lee, Eung-Hyuk
    • Journal of IKEEE
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    • v.13 no.4
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    • pp.43-46
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    • 2009
  • This paper deals with feeling recognition from people's voice to fine feature vectors. Voice signals include the people's own information and but also people's feelings and fatigues. So, many researches are being progressed to fine the feelings from people's voice. In this paper, We analysis Selectable Mode Vocoder(SMV) that is one of the standard 3GPP2 codecs of ETSI. From the analyzed result, we propose voices features for recognizing feelings. And then, feeling recognition algorithm based on gaussian mixture model(GMM) is proposed. It uses feature vectors is suggested. We verify the performance of this algorithm from changing the mixture component.

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Improving SVM with Second-Order Conditional MAP for Speech/Music Classification (음성/음악 분류 향상을 위한 2차 조건 사후 최대 확률기법 기반 SVM)

  • Lim, Chung-Soo;Chang, Joon-Hyuk
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.48 no.5
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    • pp.102-108
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    • 2011
  • Support vector machines are well known for their outstanding performance in pattern recognition fields. One example of their applications is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel scheme that improves the speech/music classification of support vector machines based on the second-order conditional maximum a priori. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. According to experimental results, the proposed algorithm shows its compatibility and potential for improving the performance of support vector machines.

Fine-tuning SVM for Enhancing Speech/Music Classification (SVM의 미세조정을 통한 음성/음악 분류 성능향상)

  • Lim, Chung-Soo;Song, Ji-Hyun;Chang, Joon-Hyuk
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.48 no.2
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    • pp.141-148
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    • 2011
  • Support vector machines have been extensively studied and utilized in pattern recognition area for years. One of interesting applications of this technique is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel approach that improves the speech/music classification of support vector machines. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. We first analyze the impact of kernel width parameter on the classifications made by support vector machines. From this analysis, we observe that we can fine-tune outputs of support vector machines with the kernel width parameter. To make the most of this capability, we identify strong correlation among neighboring input frames, and use this correlation information as a guide to adjusting kernel width parameter. According to the experimental results, the proposed algorithm is found to have potential for improving the performance of support vector machines.