Mel-Frequency Cepstral Coefficient-Based Bandwidth Extension of Narrowband Speech

被引:0
|
作者
Nour-Eldin, Amr H. [1 ]
Kabal, Peter [1 ]
机构
[1] McGill Univ, Dept Elect & Comp Engn, Montreal, PQ, Canada
关键词
Bandwidth extension; high-resolution IDCT; highband certainty; mutual information; source-filter model;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We present a novel MFCC-based scheme for the Bandwidth Extension (BWE) of narrowband speech. BWE is based on the assumption that narrowband speech (0.3-3.4 kHz) correlates closely with the highband signal (3.4-7 kHz), enabling estimation of the highband frequency content given the narrow band. While BWE schemes have traditionally used LP-based parametrizations, our recent work has shown that MFCC parametrization results in higher correlation between both bands reaching twice that using LSFs. By employing high-resolution IDCT of highband MFCCs obtained from narrowband MFCCs by statistical estimation, we achieve high-quality highband power spectra from which the time-domain speech signal can be reconstructed. Implementing this scheme for BWE translates the higher correlation advantage of MFCCs into BWE performance superior to that obtained using LSFs, as shown by improvements in log-spectral distortion as well as Itakura-based measures (the latter improving by up to 13%).
引用
收藏
页码:53 / 56
页数:4
相关论文
共 50 条
  • [41] Analysis and prediction of acoustic speech features from mel-frequency cepstral coefficients in distributed speech recognition architectures
    Darch, Jonathan
    Milner, Ben
    Vaseghi, Saeed
    Journal of the Acoustical Society of America, 2009, 124 (06): : 3989 - 4000
  • [42] Identification of Language using Mel-Frequency Cepstral Coefficients (MFCC)
    Koolagudi, Shashidhar G.
    Rastogi, Deepika
    Rao, K. Sreenivasa
    INTERNATIONAL CONFERENCE ON MODELLING OPTIMIZATION AND COMPUTING, 2012, 38 : 3391 - 3398
  • [43] Analysis and prediction of acoustic speech features from mel-frequency cepstral coefficients in distributed speech recognition architectures
    Darch, Jonathan
    Milner, Ben
    Vaseghi, Saeed
    JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 2008, 124 (06): : 3989 - 4000
  • [44] Mel-Frequency Cepstral Coefficients as Features for Automatic Speaker Recognition
    Jokic, Ivan D.
    Jokic, Stevan D.
    Delic, Vlado D.
    Peric, Zoran H.
    2015 23RD TELECOMMUNICATIONS FORUM TELFOR (TELFOR), 2015, : 419 - 424
  • [45] Using Mel-Frequency Cepstral Coefficients in Missing Data Technique
    Jun, Z. (zhj_angun@sina.com.cn), 1600, Hindawi Publishing Corporation (2004):
  • [46] Using Mel-Frequency Cepstral Coefficients in Missing Data Technique
    Zhang Jun
    Sam Kwong
    Wei Gang
    Qingyang Hong
    EURASIP Journal on Advances in Signal Processing, 2004
  • [47] Voice Recognition and Marking Using Mel-frequency Cepstral Coefficients
    Sheu, Jia-Shing
    Chen, Ching-Wen
    SENSORS AND MATERIALS, 2020, 32 (10) : 3209 - 3220
  • [48] Multiple time resolutions for derivatives of mel-frequency cepstral coefficients
    Stemmer, G
    Hacker, C
    Nöth, E
    Niemann, H
    ASRU 2001: IEEE WORKSHOP ON AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING, CONFERENCE PROCEEDINGS, 2001, : 37 - 40
  • [49] Using Mel-frequency cepstral coefficients in missing data technique
    Jun, Z
    Kwong, S
    Gang, W
    Hong, QY
    EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING, 2004, 2004 (03) : 340 - 346
  • [50] Speech Emotion Recognition using Mel Frequency Cepstral Coefficient and SVM Classifier
    Fernandes, V.
    Mascarehnas, L.
    Mendonca, C.
    Johnson, A.
    Mishra, R.
    PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON SYSTEM MODELING & ADVANCEMENT IN RESEARCH TRENDS (SMART), 2018, : 200 - 204