High accuracy and octave error immune pitch detection algorithms

The aim of this paper is to present a method improving pitch estimation accuracy, showing high performance for both synthetic harmonic signals and musical instrument sounds. This method employs an Artificial Neural Network of a feed-forward type. In addition, octave error optimized pitch detection algorithm, based on spectral analysis is introduced. The proposed algorithm is very effective for signals with strong harmonic, as well as nearly sinusoidal contents. Experiments were performed on a variety of musical instrument sounds and sample results exemplifying main issues of both engineered algorithms are shown.

[1]  W. H. Holmes,et al.  An improved harmonic-plus-noise decomposition method and its application in pitch determination , 1997, 1997 IEEE Workshop on Speech Coding for Telecommunications Proceedings. Back to Basics: Attacking Fundamental Problems in Speech Coding.

[2]  Thomas F. Quatieri,et al.  Pitch estimation and voicing detection based on a sinusoidal speech model , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[3]  T. Parks,et al.  Maximum likelihood pitch estimation , 1976 .

[4]  Eyal Yair,et al.  An accurate pitch detection algorithm , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[5]  Leah H. Jamieson,et al.  A probabilistic approach to AMDF pitch detection , 1994, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.

[6]  Lawrence R. Rabiner,et al.  On the use of autocorrelation analysis for pitch detection , 1977 .

[7]  Stephen A. Zahorian,et al.  Yet Another Algorithm for Pitch Tracking , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[8]  Andrzej Czyzewski,et al.  Representing Musical Instrument Sounds for Their Automatic Classification , 2001 .

[9]  A. Noll Cepstrum pitch determination. , 1967, The Journal of the Acoustical Society of America.

[10]  Wolfgang Hess,et al.  Pitch Determination of Speech Signals , 1983 .

[11]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[12]  Gang Xu,et al.  Pitch estimation based on Circular AMDF , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[13]  David Talkin,et al.  A Robust Algorithm for Pitch Tracking ( RAPT ) , 2005 .

[14]  Aaron E. Rosenberg,et al.  A comparative performance study of several pitch detection algorithms , 1976 .

[15]  Aaron E. Rosenberg,et al.  A subjective evaluation of pitch detection methods using LPC synthesized speech , 1977 .

[16]  Bayya Yegnanarayana,et al.  Decomposition of speech signals into deterministic and stochastic components , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[17]  Léonard Janer,et al.  Modulated Gaussian wavelet transform based speech analyser (MGWTSA) pitch detection algorithm (PDA) , 1995, EUROSPEECH.

[18]  Ramdas Kumaresan,et al.  A variable frame pitch estimator and test results , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[19]  Jian Chen,et al.  A modified pitch detection algorithm , 2001, IEEE Communications Letters.

[20]  Jeng-Shyang Pan,et al.  Efficient algorithms for speech pitch estimation , 2001, Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing. ISIMP 2001 (IEEE Cat. No.01EX489).

[21]  Tetsuya Shimamura,et al.  Robust method of measurement of fundamental frequency by ACLOS: autocorrelation of log spectrum , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.