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Quaternion Neural Networks for 3D Sound Source Localization in Reverberant Environments.

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3D-Sound-Localization

Quaternion Neural Networks for 3D Sound Source Localization: Implementation using First Order Ambisonics.

The objective of our work is to build a working deep quaternion neural network (DQNN) based network that works with First Order Ambisonics data sets. In particular, we are going to extend DQNN, adding capabilities to both support pre-existing data sets (ansim, resim, etc.) and the FOA one in a smart, modular, performing way. Therefore, other metrics have been added like the SELD score, mainly used in the 2019 paper outcomes evaluation, and a tiny library for a graphical representation of the results.

doa

seld

seld3

Usage

This project can be easily executed using one of the two proposed notebooks:

The latter gives you the possibility to use a pre-loaded and pre-extracted dataset (~200GB).

Model metrics CSV table

A quick view of our CSV files.

- description
A training_loss
B validation_loss
C sed_loss_er
D sed_loss_f1
E doa_loss_avg_accuracy
F doa_loss_gt
G doa_loss_pred
H doa_loss_gt_cnt
- description
I doa_loss_pred_cnt
J doa_loss_good_frame_cnt
K sed_score
L doa_score
M seld_score
N sed_confidence_interval_low
O sed_confidence_interval__up

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Quaternion Neural Networks for 3D Sound Source Localization in Reverberant Environments.

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