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]
- SRP-PHAT
- Steered Response Power – Phase Transform [2]
- CSSM
- Coherent Signal Subspace Method [3]
- WAVES
- Weighted Average of Signal Subspaces [4]
- TOPS
- Test of Orthogonality of Projected Subspaces [5]
- FRIDA
- Finite Rate of Innovation Direction of Arrival [6]
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', 'SRP', 'CSSM', 'WAVES', 'TOPS', 'FRIDA']
References
| [1] | R. Schmidt, Multiple emitter location and signal parameter estimation, IEEE Trans. Antennas Propag., Vol. 34, Num. 3, pp 276–280, 1986 |
| [2] | J. H. DiBiase, A high-accuracy, low-latency technique for talker localization in reverberant environments using microphone arrays, PHD Thesis, Brown University, 2000 |
| [3] | 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 |
| [4] | 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 |
| [5] | 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 |
| [6] | 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 |