Publication

Adaptation of Multiple Sound Source Localization Neural Networks with Weak Supervision and Domain-Adversarial Training

2019
Conference Papers

Abstract

Despite the recent success of deep neural network-based approaches in sound source localization, these approaches suffer the limitations that the required annotation process is costly, and the mismatch between the training and test conditions undermines the performance. This paper addresses the question of how models trained with simulation can be exploited for multiple sound source localization in real scenarios by domain adaptation. In particular, two domain adaptation methods are investigated: weak supervision and domain-adversarial training. Our experiments show that the weak supervision with the knowledge of the number of sources can significantly improve the performance of an unadapted model. However, the domain-adversarial training does not yield significant improvement for this particular problem

Official source

https://infoscience.epfl.ch/entities/publication/c725a0eb-3ff5-44ef-b802-f9dc21ef7bad

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2019
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/c725a0eb-3ff5-44ef-b802-f9dc21ef7bad

About this result

Related concepts (27)

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

Types of artificial neural networks

There are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.

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Adaptation of Multiple Sound Source Localization Neural Networks with Weak Supervision and Domain-Adversarial Training

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