The Steered Response Power Phase Transform (SRP-PHAT) is an important and robust algorithm to localize acoustic sound sources. However, the algorithm can only give us one location estimation. For multi-sources extension, we propose to use the Degraded Unmixing Estimation Technique (DUET) to separate each source and pass it to the SRP-PHAT algorithm to achieve multi-sources tracking.

>> git clone https://github.com/BrownsugarZeer/Multi_SSL.git >> cd Multi_SSL >> python -m venv venv >> venv\Scripts\activate.bat >> pip install -r requirements.txt

Pyaudio requires some tricks to install on Windows. If the installation fails, finding unofficial wheels may be a available solution.

The board is a far-field microphone array device capable of detecting voices up to 5m away even with the presence of background noise.

1. Using a microphone stream (online)

(venv) > python srp_phat_online.py -s=1 Find 1 available sources. azi: 96.2, ele: 51.8 =================================================== Find 1 available sources. azi: 129.8, ele: 51.2 =================================================== Find 1 available sources. azi: 54.0, ele: 30.5 =================================================== ...

2. Using an audio file (offline)

# Automatically determine the number of sources (venv) > python srp_phat_offline.py -s=1 -c=4 -i=None --wave=data/a180e20/50cm/a180e19_3_1b6ede00.wav Find 1 available sources. azi: 179.7, ele: 22.7 (venv) > python srp_phat_offline.py -s=3 -c=4 -i=None --wave=data/a180e20_a225e35_a270e50/250cm/a180e19_a224e34_a269e49_3_1c186780.wav Find 3 available sources. azi: 223.2, ele: 44.9 azi: 266.2, ele: 51.2 azi: 184.7, ele: 22.0

You can test the DUET algorithm by running the script in tests folder. Here is the results of the test.

To easily show what's going on, we use plotly to plot the DOA on a sphere which diameter is 1 meter. The center of the sphere is the microphone array we place at p(x=0, y=0, z=0), the dark blue dots are the Directions of Arrival (DOA), and the lighter dots are the projections on each plane.

(venv) > python srp_visualizer.py -s=1 --wav=data/a180e20/50cm.csv

50cm

150cm

250cm

1. The algorithm has a high computational complexity thus making the algorithm unsuitable for real time applications. For estimating one source we need at least 0.3 seconds, estimating N sources we need at least (0.3*N) seconds,

1. S. Rickard, "The DUET blind source separation algorithm." Blind Speech Separation, pp. 217-241, 2007.

2. Dey, Ajoy Kumar, and Susmita Saha. "Acoustic Beamforming: Design and Development of Steered Response Power With Phase Transformation (SRP-PHAT)." (2011).

3. Ravanelli, Mirco, et al. "SpeechBrain: A General-Purpose Speech Toolkit." arXiv preprint arXiv:2106.04624 (2021).