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Autocorrelation Sequence

The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform

T. Ohmori – 1st expert on this subject based on the ideXlab platform

  • A method for extracting a musical unit to phrase music data in the compressed domain of TwinVQ audio compression
    2005 IEEE International Conference on Multimedia and Expo, 2005
    Co-Authors: M. Nakanishi, M. Kobayakawa, M. Hoshi, T. Ohmori

    Abstract:

    A method for phrasing music data into meaningful musical pieces (e.g., bar and phrase) is an important function to analyze music data. To realize this function, we propose a method for extracting a unit of music data (musical unit) in the compressed domain of TwinVQ audio compression (MPEG-4 audio). Our key idea is to extract a musical unit from a Sequence of Autocorrelation coefficients computed in the encoding step of TwinVQ audio compression. We call the Sequence of the Autocorrelation coefficients the “Autocorrelation Sequence r”. We use the k-th Autocorrelation Sequence r/sub k/ (k=1, 2, …, 20) of music data for extracting a musical unit of music data. First, we calculate the j/sub k/-th Autocorrelation coefficient a/sub k//sup j//sub k/ of the k-th Autocorrelation Sequence r/sub k/ (j/sub k/=38, 39, …, 208; k=1, 2, …,20). Second, for detecting the peak in the Sequence (a/sub k//sup 38/, a/sub k//sup 39/, …, a/sub k//sup 208/), the Laplacian filter is applied to the Sequence. We then obtain the order p/sub k/ for which the maximum differential coefficient is attained. Finally, we compute the musical unit using p/sub k/. To evaluate the performance of extracting the musical unit by our method, we collected 64 music data and obtained Autocorrelation Sequences by applying the TwinVQ encoder to each data. We then applied our extraction algorithm to each Autocorrelation Sequence. The experimental results reveal a very good performance in the extraction of a musical unit for phrasing music data.

  • ICME – A method for extracting a musical unit to phrase music data in the compressed domain of TwinVQ audio compression
    2005 IEEE International Conference on Multimedia and Expo, 2005
    Co-Authors: M. Nakanishi, M. Kobayakawa, M. Hoshi, T. Ohmori

    Abstract:

    A method for phrasing music data into meaningful musical pieces (e.g., bar and phrase) is an important function to analyze music data. To realize this function, we propose a method for extracting a unit of music data (musical unit) in the compressed domain of TwinVQ audio compression (MPEG-4 audio). Our key idea is to extract a musical unit from a Sequence of Autocorrelation coefficients computed in the encoding step of TwinVQ audio compression. We call the Sequence of the Autocorrelation coefficients the “Autocorrelation Sequence r”. We use the k-th Autocorrelation Sequence r/sub k/ (k=1, 2, …, 20) of music data for extracting a musical unit of music data. First, we calculate the j/sub k/-th Autocorrelation coefficient a/sub k//sup j//sub k/ of the k-th Autocorrelation Sequence r/sub k/ (j/sub k/=38, 39, …, 208; k=1, 2, …,20). Second, for detecting the peak in the Sequence (a/sub k//sup 38/, a/sub k//sup 39/, …, a/sub k//sup 208/), the Laplacian filter is applied to the Sequence. We then obtain the order p/sub k/ for which the maximum differential coefficient is attained. Finally, we compute the musical unit using p/sub k/. To evaluate the performance of extracting the musical unit by our method, we collected 64 music data and obtained Autocorrelation Sequences by applying the TwinVQ encoder to each data. We then applied our extraction algorithm to each Autocorrelation Sequence. The experimental results reveal a very good performance in the extraction of a musical unit for phrasing music data.

Jianhao Hu – 2nd expert on this subject based on the ideXlab platform

  • Random Error Reduction Scheme for Combinational Stochastic Circuit
    Mathematical Problems in Engineering, 2017
    Co-Authors: Ye Cheng, Jianhao Hu

    Abstract:

    In conventional stochastic computation, all the input streams are Bernoulli Sequences (BSs), which may result in large random error. To reduce random error and improve computational accuracy, some other Sequences have been reported as alternatives to BSs. However, these Sequences only apply to the specific stochastic circuits, have difficulties in hardware generation, or have length constraints. To this end, new Sequences without these disadvantages should be considered. This paper proposes the random error analysis method for stochastic computation based on Autocorrelation Sequence (AS), which is more general than the conventional one based on BS. The analysis results show that we can use the proper ASs as input streams of stochastic circuits to reduce random error. On the basis of that conclusion, we propose the random error reduction scheme based on maximal concentrated Autocorrelation Sequence (MCAS) and BS, both of which are ASs. MCAS and BS are applicable to any combinational stochastic circuit, are easily generated by hardware, and have no length constraints, which avoid the disadvantages of Sequences in the previous work. Moreover, we apply the proposed random error reduction scheme into several typical stochastic circuits as case studies. The simulation results confirm the effectiveness of the proposed scheme.

  • Random error analysis and reduction for stochastic computation based on Autocorrelation Sequence
    2014 IEEE International Symposium on Circuits and Systems (ISCAS), 2014
    Co-Authors: Ye Cheng, Jianhao Hu

    Abstract:

    This paper proposes the random error analysis method for stochastic computation based on Autocorrelation Sequence (AS), which is more general than the previous work based on Bernoulli Sequence (BS). The analysis results show the use of proper ASs as input streams is able to reduce random error compared to the conventional use of BSs. In order to confirm that conclusion, we apply an AS, referred as Maximal Concentrated Autocorrelation Sequence (MCAS), into the stochastic computation system which implements Bernstein polynomial. Both the theoretical analysis and simulation results reveal that the use of MCAS reduces the random error.

  • ISCAS – Random error analysis and reduction for stochastic computation based on Autocorrelation Sequence
    2014 IEEE International Symposium on Circuits and Systems (ISCAS), 2014
    Co-Authors: Ye Cheng, Jianhao Hu

    Abstract:

    This paper proposes the random error analysis method for stochastic computation based on Autocorrelation Sequence (AS), which is more general than the previous work based on Bernoulli Sequence (BS). The analysis results show the use of proper ASs as input streams is able to reduce random error compared to the conventional use of BSs. In order to confirm that conclusion, we apply an AS, referred as Maximal Concentrated Autocorrelation Sequence (MCAS), into the stochastic computation system which implements Bernstein polynomial. Both the theoretical analysis and simulation results reveal that the use of MCAS reduces the random error.

C. Nadeu – 3rd expert on this subject based on the ideXlab platform

  • Linear prediction of the one-sided Autocorrelation Sequence for noisy speech recognition
    IEEE Transactions on Speech and Audio Processing, 1997
    Co-Authors: J. Hernando, C. Nadeu

    Abstract:

    The article presents a robust representation of speech based on AR modeling of the causal part of the Autocorrelation Sequence. In noisy speech recognition, this new representation achieves better results than several other related techniques.

  • Pitch determination using the cepstrum of the one-sided Autocorrelation Sequence
    [Proceedings] ICASSP 91: 1991 International Conference on Acoustics Speech and Signal Processing, 1991
    Co-Authors: C. Nadeu, J. Pascual, J. Hernando

    Abstract:

    A novel cepstral function, the cepstrum (CEP) of the one-sided Autocorrelation Sequence (COSA), is presented and applied to pitch determination of speech signals. This pitch determination algorithm (PDA) starts from the Autocorrelation Sequence in lieu of the speech signal. Although the COSA pitch determination algorithm does not improve the performance of the Autocorrelation-with-center-clipping and CEP algorithms in quasiperiodic speech frames, it significantly reduces their pitch-period errors at transitional speech segments as well as in speech signals contaminated by noise. The PDA’s better performance is based on its accuracy at nonstationary segments of speech signals and its noise capability.

  • ICASSP – Pitch determination using the cepstrum of the one-sided Autocorrelation Sequence
    [Proceedings] ICASSP 91: 1991 International Conference on Acoustics Speech and Signal Processing, 1991
    Co-Authors: C. Nadeu, J. Pascual, J. Hernando

    Abstract:

    A novel cepstral function, the cepstrum (CEP) of the one-sided Autocorrelation Sequence (COSA), is presented and applied to pitch determination of speech signals. This pitch determination algorithm (PDA) starts from the Autocorrelation Sequence in lieu of the speech signal. Although the COSA pitch determination algorithm does not improve the performance of the Autocorrelation-with-center-clipping and CEP algorithms in quasiperiodic speech frames, it significantly reduces their pitch-period errors at transitional speech segments as well as in speech signals contaminated by noise. The PDA’s better performance is based on its accuracy at nonstationary segments of speech signals and its noise capability. >