dataandsignal / libmusic Goto Github PK

Hello!

I would like to share a news that I started a work on a new version of libmusic.
This comes as a result of my recent re-research of the MUSIC, which comes as a result of my long lasting desire to answer all the questions about this algorithm that I had left without knowing the answers, as a result of my engineering thesis and because I want to use libmusic in a bunch of new cases.

There will be plenty of improvements including:

• specify (pure) signal and noise space dimensions (you can choose number of signal and noise vectors separately)
• get power and amplitude estimates
• get frequency estimates by rooting each eigenfilter (computing Z-transform of eigenvector and finding it's complex roots)
• buffer a single frame in lmtool and run detection on each sample
• code refactoring so DTMF related code won't confuse people anymore

Please share your comments, ideas, requests and questions!

The work is currently at a MATLAB stage and you can trace it here

Greetings! I'm trying to make the mentioned combo work together, EmbeddedLapack contains f2c-converted blas and lapack libs. Tests with dgesvd_ (6, 7) keep failing, test 2,3 with dgesvd are ok. VT and S matrixes differs from reference ones in 6,7. Correlation matrix is ok everywhere. So it seems that dgesvd breaks with bigger matrixes OR data is incorrectly handled between correlation and dgesvd. Could you, please, tell, why should we shuffle data in such way (is it transpose or smth else)? Sorry for stupid questions and thanks in advance!
for (i = 0; i < M; ++i) { for (j = 0; j < N; ++j) { A[j * M + i] = Y[i * yn + j]; } }

Hello.
I am trying to implement a low memory Music algorithm to separate breathing and heart rate signals, which are 0.5Hz and 1-2Hz, on an ARM processor.

After some filtering I arrived to your implementation of Music, but I have seen that it is mostly prepared for 8kHz and 16 sample signals, and I am using 56 Hz as Fs and 200-500 sample points FFTs.

Would it be too difficult or memory dependant to adapt this library?
Thank you