Direction of Arrival

Module contents

Direction of Arrival Finding

This sub-package provides implementations of popular direction of arrival findings algorithms.

MUSIC
Multiple Signal Classification 1
pyroomacoustics.doa.music
NormMUSIC
MUSIC with frequency normalization 2
pyroomacoustics.doa.normmusic
SRP-PHAT
Steered Response Power – Phase Transform 3
pyroomacoustics.doa.srp
CSSM
Coherent Signal Subspace Method 4
pyroomacoustics.doa.cssm
WAVES
Weighted Average of Signal Subspaces 5
pyroomacoustics.doa.waves
TOPS
Test of Orthogonality of Projected Subspaces 6
pyroomacoustics.doa.tops
FRIDA
Finite Rate of Innovation Direction of Arrival 7
pyroomacoustics.doa.frida

All these classes derive from the abstract base class pyroomacoustics.doa.doa.DOA that offers generic methods for finding and visualizing the locations of acoustic sources.

The constructor can be called once to build the DOA finding object. Then, the method pyroomacoustics.doa.doa.DOA.locate_sources performs DOA finding based on time-frequency passed to it as an argument. Extra arguments can be supplied to indicate which frequency bands should be used for localization.

How to use the DOA module

Here R is a 2xQ ndarray that contains the locations of the Q microphones in the columns, fs is the sampling frequency of the input signal, and nfft the length of the FFT used.

The STFT snapshots are passed to the localization methods in the X ndarray of shape Q x (nfft // 2 + 1) x n_snapshots, where n_snapshots is the number of STFT frames to use for the localization. The option freq_bins can be provided to specify which frequency bins to use for the localization.

>>> doa = pyroomacoustics.doa.MUSIC(R, fs, nfft)
>>> doa.locate_sources(X, freq_bins=np.arange(20, 40))

Other Available Subpackages

pyroomacoustics.doa.grid

this provides abstractions for computing functions on regular or irregular grids defined on circles and spheres with peak finding methods

pyroomacoustics.doa.plotters

a few methods to plot functions and points on circles or spheres

pyroomacoustics.doa.detect_peaks

1D peak detection routine from Marcos Duarte

pyroomacoustics.doa.tools_frid_doa_plane

routines implementing FRIDA algorithm

Utilities

pyroomacoustics.doa.algorithms

a dictionary containing all the DOA object subclasses availables indexed by keys ['MUSIC', 'NormMUSIC', 'SRP', 'CSSM', 'WAVES', 'TOPS', 'FRIDA']

Note on MUSIC

Since NormMUSIC has a more robust performance, we recommend to use NormMUSIC over MUSIC. When MUSIC is used as a baseline for publications, we recommend to use both NormMUSIC and MUSIC. For more information, you may have a look at our jupyter notebook at https://github.com/LCAV/pyroomacoustics/tree/master/notebooks/norm_music_demo.ipynb

References

1

R. Schmidt, Multiple emitter location and signal parameter estimation, IEEE Trans. Antennas Propag., Vol. 34, Num. 3, pp 276–280, 1986

2

D. Salvati, C. Drioli, G. L. Foresti, Incoherent Frequency Fusion for Broadband Steered Response Power Algorithms in Noisy Environments, IEEE Signal Process. Lett., Vol. 21, Num. 5, pp 581-585, 2014

3

J. H. DiBiase, A high-accuracy, low-latency technique for talker localization in reverberant environments using microphone arrays, PHD Thesis, Brown University, 2000

4

H. Wang, M. Kaveh, Coherent signal-subspace processing for the detection and estimation of angles of arrival of multiple wide-band sources, IEEE Trans. Acoust., Speech, Signal Process., Vol. 33, Num. 4, pp 823–831, 1985

5

E. D. di Claudio, R. Parisi, WAVES: Weighted average of signal subspaces for robust wideband direction finding, IEEE Trans. Signal Process., Vol. 49, Num. 10, 2179–2191, 2001

6

Y. Yeo-Sun, L. M. Kaplan, J. H. McClellan, TOPS: New DOA estimator for wideband signals, IEEE Trans. Signal Process., Vol. 54, Num 6., pp 1977–1989, 2006

7

H. Pan, R. Scheibler, E. Bezzam, I. Dokmanic, and M. Vetterli, FRIDA: FRI-based DOA estimation for arbitrary array layouts, Proc. ICASSP, pp 3186-3190, 2017

Algorithms

Tools and Helpers