• Title/Summary/Keyword: Spectral Entropy

Search Result 60, Processing Time 0.024 seconds

Voice Activity Detection Based on Entropy in Noisy Car Environment (차량 잡음 환경에서 엔트로피 기반의 음성 구간 검출)

  • Roh, Yong-Wan;Lee, Kue-Bum;Lee, Woo-Seok;Hong, Kwang-Seok
    • Journal of the Institute of Convergence Signal Processing
    • /
    • v.9 no.2
    • /
    • pp.121-128
    • /
    • 2008
  • Accurate voice activity detection have a great impact on performance of speech applications including speech recognition, speech coding, and speech communication. In this paper, we propose methods for voice activity detection that can adapt to various car noise situations during driving. Existing voice activity detection used various method such as time energy, frequency energy, zero crossing rate, and spectral entropy that have a weak point of rapid. decline performance in noisy environments. In this paper, the approach is based on existing spectral entropy for VAD that we propose voice activity detection method using MFB(Met-frequency filter banks) spectral entropy, gradient FFT(Fast Fourier Transform) spectral entropy. and gradient MFB spectral entropy. FFT multiplied by Mel-scale is MFB and Mel-scale is non linear scale when human sound perception reflects characteristic of speech. Proposed MFB spectral entropy method clearly improve the ability to discriminate between speech and non-speech for various in noisy car environments that achieves 93.21% accuracy as a result of experiments. Compared to the spectral entropy method, the proposed voice activity detection gives an average improvement in the correct detection rate of more than 3.2%.

  • PDF

Speech Emotion Recognition Based on GMM Using FFT and MFB Spectral Entropy (FFT와 MFB Spectral Entropy를 이용한 GMM 기반의 감정인식)

  • Lee, Woo-Seok;Roh, Yong-Wan;Hong, Hwang-Seok
    • Proceedings of the KIEE Conference
    • /
    • 2008.04a
    • /
    • pp.99-100
    • /
    • 2008
  • This paper proposes a Gaussian Mixture Model (GMM) - based speech emotion recognition methods using four feature parameters; 1) Fast Fourier Transform(FFT) spectral entropy, 2) delta FFT spectral entropy, 3) Mel-frequency Filter Bank (MFB) spectral entropy, and 4) delta MFB spectral entropy. In addition, we use four emotions in a speech database including anger, sadness, happiness, and neutrality. We perform speech emotion recognition experiments using each pre-defined emotion and gender. The experimental results show that the proposed emotion recognition using FFT spectral-based entropy and MFB spectral-based entropy performs better than existing emotion recognition based on GMM using energy, Zero Crossing Rate (ZCR), Linear Prediction Coefficient (LPC), and pitch parameters. In experimental Results, we attained a maximum recognition rate of 75.1% when we used MFB spectral entropy and delta MFB spectral entropy.

  • PDF

A Study on the Realization of Wireless Home Network System Using High-performance Speech Recognition in Variable Position (가변위치 고음성인식 기술을 이용한 무선 홈 네트워크 시스템 구현에 관한 연구)

  • Yoon, Jun-Chul;Choi, Sang-Bang;Park, Chan-Sub;Kim, Se-Yong;Kim, Ki-Man;Kang, Suk-Youb
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.14 no.4
    • /
    • pp.991-998
    • /
    • 2010
  • In realization of wireless home network system using speech recognition in indoor voice recognition environment, background noise and reverberation are two main causes of digression in voice recognition system. In this study, the home network system resistant to reverberation and background noise using voice section detection method based on spectral entropy in indoor recognition environment is to be realized. Spectral subtraction can reduce the effect of reverberation and remove noise independent from voice signal by eliminating signal distorted by reverberation in spectrum. For effective spectral subtraction, the correct separation of voice section and silent section should be accompanied and for this, improvement of performance needs to be done, applying to voice section detection method based on entropy. In this study, experimental and indoor environment testing is carried out to figure out command recognition rate in indoor recognition environment. The test result shows that command recognition rate improved in static environment and reverberant room condition, using voice section detection method based on spectral entropy.

Vocal Effort Detection Based on Spectral Information Entropy Feature and Model Fusion

  • Chao, Hao;Lu, Bao-Yun;Liu, Yong-Li;Zhi, Hui-Lai
    • Journal of Information Processing Systems
    • /
    • v.14 no.1
    • /
    • pp.218-227
    • /
    • 2018
  • Vocal effort detection is important for both robust speech recognition and speaker recognition. In this paper, the spectral information entropy feature which contains more salient information regarding the vocal effort level is firstly proposed. Then, the model fusion method based on complementary model is presented to recognize vocal effort level. Experiments are conducted on isolated words test set, and the results show the spectral information entropy has the best performance among the three kinds of features. Meanwhile, the recognition accuracy of all vocal effort levels reaches 81.6%. Thus, potential of the proposed method is demonstrated.

Mixed $H_2/H_{\infty}$ Controller Realization with Entropy Integral

  • Lee, Sang-Hyuk;Kim, Ju-Sik
    • International Journal of Control, Automation, and Systems
    • /
    • v.1 no.2
    • /
    • pp.206-209
    • /
    • 2003
  • An $H_2$/$H_{\infty}$ -controller realization is carried out by considering an entropy integral. Using J-spectral factorization, the parametrizations of all $H_{\infty}$ stabilizing controllers are derived. By the relation of a mixed $H_2$/$H_{\infty}$ control problem and a minimum entropy/$H_{\infty}$ control problem, the mixed $H_2$/$H_{\infty}$-controller state-space realization is presented.

On the Computational Efficiency and Stableness of Burg's Algorithm for Maximum Entropy Spectral Analysis (최대엔트로피 스펙트럼 분석에 관한 Burg알고리즘의 계산효율과 안정성에 대하여)

  • Kim, Hee Joon
    • Economic and Environmental Geology
    • /
    • v.17 no.4
    • /
    • pp.237-243
    • /
    • 1984
  • Burg's algorithm for maximum entropy spectral analysis is studied with respect to its computational efficiency and stableness. The Burg's method is not only less efficient than the Yule-Walker's method but also sometimes unstable due to its mathematical irrationality. This irrationality is demonstrated by analyzing an artificial time series, and more stable and effective method is proposed. An efficient procedure using Goertzel's algorithm to compute power spectral densities is also proposed.

  • PDF

Voice Activity Detection Based on Signal Energy and Entropy-difference in Noisy Environments (엔트로피 차와 신호의 에너지에 기반한 잡음환경에서의 음성검출)

  • Ha, Dong-Gyung;Cho, Seok-Je;Jin, Gang-Gyoo;Shin, Ok-Keun
    • Journal of Advanced Marine Engineering and Technology
    • /
    • v.32 no.5
    • /
    • pp.768-774
    • /
    • 2008
  • In many areas of speech signal processing such as automatic speech recognition and packet based voice communication technique, VAD (voice activity detection) plays an important role in the performance of the overall system. In this paper, we present a new feature parameter for VAD which is the product of energy of the signal and the difference of two types of entropies. For this end, we first define a Mel filter-bank based entropy and calculate its difference from the conventional entropy in frequency domain. The difference is then multiplied by the spectral energy of the signal to yield the final feature parameter which we call PEED (product of energy and entropy difference). Through experiments. we could verify that the proposed VAD parameter is more efficient than the conventional spectral entropy based parameter in various SNRs and noisy environments.

TOPOLOGICAL ENTROPY OF SWITCHED SYSTEMS

  • Huang, Yu;Zhong, Xingfu
    • Journal of the Korean Mathematical Society
    • /
    • v.55 no.5
    • /
    • pp.1157-1175
    • /
    • 2018
  • For a switched system with constraint on switching sequences, which is also called a subshift action, on a metric space not necessarily compact, two kinds of topological entropies, average topological entropy and maximal topological entropy, are introduced. Then we give some properties of those topological entropies and estimate the bounds of them for some special systems, such as subshift actions generated by finite smooth maps on p-dimensional Riemannian manifold and by a family of surjective endomorphisms on a compact metrizable group. In particular, for linear switched systems on ${\mathbb{R}}^p$, we obtain a better upper bound, by joint spectral radius, which is sharper than that by Wang et al. in [42,43].

Performance Enhancement of Speech Communication System using Reverberation Rejection (잔향제거를 이용한 음성통신 시스템 성능 향상)

  • Kim, Se-Young;Kang, Suk-Youb;Kim, Ki-Man
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.13 no.10
    • /
    • pp.2211-2217
    • /
    • 2009
  • In this paper, we propose the speech enhancement algorithm using an one-microphone in a reverberant room environments. Spectral subtraction is the effective method which can reduce the reverberation element and the noise in a spectrum domain. Spectral subtraction needs correct separation of voice section and silent section therefore to improve the performance, voice activity detection(VAD) based on entropy has been applied to the proposed method. We test a performance of the proposed method by comparing with conventional method which used VAD based on energy detection. Reverberation reduction ratio with variable of SNR and a reverberation time is used as a test index. From the simulation result, proposed method shows performance better than conventional method.

Reconstruction Effect of the Spectral Entropy for the Voice Activity Detection (음성 활동 구간 검출을 위한 스펙트랄 엔트로피의 재구성 효과)

  • Kwon HO-Min;Han Hag-Yong;Lee Kwang-Seok;Koh Si-Young;Hur Kang-In
    • Proceedings of the Acoustical Society of Korea Conference
    • /
    • spring
    • /
    • pp.25-28
    • /
    • 2002
  • Voice activity detection is important Problem in the speech recognition and communication. This paper introduces feature parameter which is reconstructed by the spectral entropy of information theory for the robust voice activity detection in the noise environment, analyzes and compares it with the energy method of voice activity detection and performance. In experiment, we confirmed that the spectral entropy is more feature parameter than the energy method for the robust voice activity detection in the various noise environment.

  • PDF