Robust low-resource sound localization in correlated noise

Netsch, Lorin; Stachurski, Jacek

doi:10.21437/interspeech.2014-506

Cited by 1 publication

(1 citation statement)

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“…high signal-to-noise ratio, known number of sources, low reverberation, etc. Such ideal conditions hardly hold true in real-world applications and usually require special treatments [5][6][7][8]. Recently, data-driven approaches and in particular deep learning have outperformed classical signal processing methods for various audio tasks [9,10] including SSL [11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Ensemble of Discriminators for Domain Adaptation in Multiple Sound Source 2D Localization

Moing,

Agravante,

Inoue

et al. 2020

Preprint

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This paper introduces an ensemble of discriminators that improves the accuracy of a domain adaptation technique for the localization of multiple sound sources. Recently, deep neural networks have led to promising results for this task, yet they require a large amount of labeled data for training. Recording and labeling such datasets is very costly, especially because data needs to be diverse enough to cover different acoustic conditions. In this paper, we leverage acoustic simulators to inexpensively generate labeled training samples. However, models trained on synthetic data tend to perform poorly with real-world recordings due to the domain mismatch. For this, we explore two domain adaptation methods using adversarial learning for sound source localization which use labeled synthetic data and unlabeled real data. We propose a novel ensemble approach that combines discriminators applied at different feature levels of the localization model. Experiments show that our ensemble discrimination method significantly improves the localization performance without requiring any label from the real data.

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