**Owning Class: **spectral analysis

**Requires: **MathScript RT Module

## Syntax

[w, pw] = root_music(x, order)

[w, pw] = root_music(x, order, 'corr')

[f, pw] = root_music(x, order, fs)

[f, pw] = root_music(x, order, fs, 'corr')

Legacy Name: `rootmusic`

## Description

Uses the multiple signal classification (MUSIC) algorithm to calculate the frequency and power components of the input signal.

Examples

## Inputs

Name |
Description |

**x** |
Specifies the input signal. **x** can be a real or complex vector or matrix. If you specify **'corr'**, **x** is a square autocorrelation matrix. Otherwise, **x** is a matrix whose elements are stacked row-wise. |

**order** |
Specifies the dimension information of the signal subspace. **order** can be a scalar or a vector of two elements. If **order** is a scalar, **order** specifies the dimension of the signal subspace. If **order** is a vector of two elements, **order**(2) separates the signal and noise subspace. All eigenvalues greater than **order**(2)*the smallest eigenvalue belong to the signal subspace, and the signal subspace dimension is less than or equal to **order**(1). |

**fs** |
Specifies the sampling frequency of the input sequence in Hz. If you specify **fs**, LabVIEW returns the output frequencies in Hz. Otherwise, LabVIEW returns the output frequencies in radians per sample. |

**'corr'** |
Specifies that **x** is a square autocorrelation matrix. |

## Outputs

Name |
Description |

**w** |
Returns the estimated radius frequencies of the sinusoids. **w** ranges from 0 to pi. |

**f** |
Returns the estimated frequencies of the sinusoids. **f** ranges from 0 to **fs** / 2. |

**pw** |
Returns the estimated powers of the estimated sinusoids. |

## Examples

fs = 1000;

t = 0:1/fs:0.2;

noise = randnormal(size(t));

x = sin(2*pi*100*t) + cos(2*pi*250*t) + noise;

[f, pw] = root_music(x, 4, fs);

## Related Topics

pspec_eign

pspec_music

root_eign