(Note for those wondering what MUSIC is: G. Arslan, B. L. Evans, F. A. Sakarya, and J. L. Pino, "Performance Evaluation and Real-Time Implementation of Subspace, Adaptive, and DFT Algorithms for Multi-Tone Detection," Proc. Int. Conf. on Telecommunications, Istanbul, Turkey, April 15-17, 1996. says:
"MUSIC detects frequencies in a signal by performing an eigen decomposition on the covariance matrix of a data vector y of M samples obtained from the samples of the received signal.") I set up a web page that includes pdf and png files of the two Ptolemy Classic MUSIC demos and the original ptlang definition of the MUSIC actor, see: http://ptolemy.eecs.berkeley.edu/ptolemyclassic/music/index.htm Below is the ptlang definition from Ptolemy Classic, found in ptolemy/src/domains/sdf/dsp/stars/SDFMUSIC_M.cc --start-- defstar { name { MUSIC_M } domain { SDF } version { @(#)SDFMUSIC_M.pl 1.18 06 Oct 1996 } author { Mike J. Chen } copyright { Copyright (c) 1993-1996 The Regents of the University of California. All rights reserved. See the file $PTOLEMY/copyright for copyright notice, limitation of liability, and disclaimer of warranty provisions. } location { SDF dsp library } descriptor { This star is used to estimate the frequencies of some specified number of sinusoids in a signal. The output is the eigenspectrum of a signal such that the locations of the peaks of the eigenspectrum correspond to the frequencies of sinusoids in the signal. The input is the right singular vectors in the form generated by the SVD_M star. The MUSIC algorithm is used, where MUSIC stands for "multiple signal classification." } htmldoc { The MUSIC algorithm is a general-purpose algorithm for estimating the presence of sinusoids buried in white noise [1-2]. MUSIC provides high-resolution spectral estimates [3-4] which can be used in source localization in spatial array processing [4-5]. Several derivatives of MUSIC exist such as root-MUSIC [6]. <a name="Dudgeon, D. E."></a> <a name="Friedlander, B."></a> <a name="Haykin, S."></a> <a name="Johnson, D. H."></a> <a name="Nehorai, A."></a> <a name="Stoica, P."></a> <a name="Soderstrom, B."></a> <a name="Weiss, A. J."></a> <h3>References</h3> <p>[1] S. Haykin, ed., <i>Advances in Spectrum Analysis and Array Processing</i>, vol. 2, Prentice-Hall: Englewood Cliffs, NJ, 1991. <p>[2] S. Haykin, <i>Adaptive Filter Theory</i>, Prentice-Hall: Englewood Cliffs, NJ. 1991. 2nd ed. <p>[3] P. Stoica and A. Nehorai, ``MUSIC, Maximum Likelihood, and Cramer-Rao Bound: Further Results and Comparisons,'' <i>IEEE Trans. on Acoustics, Speech, and Signal Processing</i>, vol. 38, pp. 2140-2150, Dec. 1990. <p>[4] D. H. Johnson and D. E. Dudgeon, <i>Array Signal Processing</i>, Prentice-Hall: Englewood Cliffs, NJ. 1993. <p>[5] P. Stoica and B. Soderstrom, ``Statistical Analysis of MUSIC and Subspace Rotation Estimates of Sinusoidal Frequencies,'' <i>IEEE Trans. on Acoustics, Speech, and Signal Processing</i>, vol. 39, pp. 1122-1135, Aug. 1991. <p>[6] B. Friedlander and A. J. Weiss, ``Direction Finding Using Spatial Smoothing With Interpolated Arrays,'' <i>IEEE Trans. on Aerospace and Electronic Systems</i>, vol. 28, no. 2, pp. 574-587, April 1992. } input { name { rsvec } type { FLOAT_MATRIX_ENV } desc { Right singular vectors. } } output { name { output } type { float } desc { S(w) eigenspectrum points. } } defstate { name { numRows } type { int } default { 4 } desc { The number of rows in the right singular matrix V. } } defstate { name { numCols } type { int } default { 4 } desc { The number of columns in the right singular matrix V. } } defstate { name { numSignals } type { int } default { 1 } desc { The number of unique signals we are trying to locate. } } defstate { name { resolution } type { int } default { 256 } desc { The number of points in the frequency domain. } } hinclude { <math.h>, "Matrix.h" } protected { ComplexMatrix *e; // e(w) is the comple frequency search vector FloatMatrix *Vn; // matrix of the right singular vectors of the noise FloatMatrix *VnVnt; // Matrix that is the product of Vn * transpose(Vn) int numNoise; // Number of right singular vectors of the noise subspace int nrows; // Number of rows in X int ncols; // Number of columns in X ComplexMatrix *F; // F(w) = eh(w)*VnVnt*e(w) double result; // The final calculated value of the amplitude of S(w) } destructor { delete e; delete Vn; delete VnVnt; delete F; } constructor { e = F = 0; Vn = VnVnt = 0; } setup { nrows = int(numRows); ncols = int(numCols); numNoise = ncols - 2*int(numSignals); // delete previously allocated arrays delete e; delete Vn; delete VnVnt; delete F; // allocate new arrays e = new ComplexMatrix(nrows,1); Vn = new FloatMatrix(nrows,numNoise); VnVnt = new FloatMatrix(nrows,nrows); F = new ComplexMatrix(1,1); // set the number of outputs output.setSDFParams(int(resolution)+1); } go { // assumes that the singular values and the associated right singular // vectors have been sorted in descending order // load matrix of right singular vectors of the noise subspace Envelope inputPkt; (rsvec%0).getMessage(inputPkt); // check for empty input if(inputPkt.empty()) { FloatMatrix& V = *(new FloatMatrix(int(numRows),int(numCols))); V = 0.0; for(int row = 0; row < nrows; row++) for(int col = 0; col < numNoise; col++) (*Vn)[row][col] = V[row][col+(2*int(numSignals))]; delete &V; } else { const FloatMatrix& V = *(const FloatMatrix *)inputPkt.myData(); for(int row = 0; row < nrows; row++) for(int col = 0; col < numNoise; col++) (*Vn)[row][col] = V[row][col+(2*int(numSignals))]; } // compute VnVnt = Vn * transpose(Vn) *VnVnt = (*Vn) * (~(*Vn)); double w = -(M_PI); // set omega to scan from -PI to PI for(int count = 0; count <= int(resolution); count++) { // compute e(w), the frequency-searching vector, given an w // and eh(w), the hermitian transpose of e(w) for(int row = 0; row < nrows; row++) (*e)[row][0] = Complex(cos(row*w),sin(row*w)); // calculate the real part of F(w) = eh(w)*VnVnt*e(w) *F = (e->hermitian()) * ComplexMatrix(*VnVnt) * (*e); // calculate the amplitude of S(w) result = 1/abs((*F)[0][0]); output%(count) << result; w += 2*(M_PI)/int(resolution); } } } --end-- It should be fairly straight forward to create a Ptolemy II actor from the above description. The webpage includes links to Matrix.h and Matrix.cc, which is where the ComplexMatrix class is declared and implemented. -Christopher -------- I mean music algorithm like in http://ptolemy.eecs.berkeley.edu/papers/96/dtmf_ict/www/node5.html#SECTION00050000000000000000, Found with keywords "music algorithm" http://ptolemy.eecs.berkeley.edu/search/ Regards, Alex -----Original Message----- From: Edward A Lee [mailto:[EMAIL PROTECTED] Sent: Thursday, September 02, 2004 6:32 PM To: Alexander Mikhalev Cc: [EMAIL PROTECTED] Subject: RE: 2 actors with TransmitPropertyTransformer At 12:30 PM 9/2/2004 +0100, Alexander Mikhalev wrote: >There was an implementation of the music algorithm in the Ptolemy >classic, is anything like this in the ptII? I'm not sure which music algorithm you are referring to, but in the "complete list of demos" (under "quick tour") there are: - Karplus-Strong sound synthesis - SynthesizedVoice (a co-articulation demo) - SoundSpectrum Edward ------------ Edward A. Lee, Professor 518 Cory Hall, UC Berkeley, Berkeley, CA 94720 phone: 510-642-0455, fax: 510-642-2739 [EMAIL PROTECTED], http://ptolemy.eecs.berkeley.edu/~eal --------------------------------------------------------------------------- - Posted to the ptolemy-hackers mailing list. Please send administrative mail for this list to: [EMAIL PROTECTED] -------- ---------------------------------------------------------------------------- Posted to the ptolemy-hackers mailing list. Please send administrative mail for this list to: [EMAIL PROTECTED]
