Semi-supervised source localization in reverberant environments with deep generative modeling

Bianco, Michael J.; Gannot, Sharon; Fernández-Grande, Efrén; Gerstoft, Peter

doi:10.48550/arxiv.2101.10636

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…In particular, ML approaches might betteraddress the inherent non-linearity in sound localization. Neural network classifier [31], [32], [25], [33] or semi supervised learning [34], [35], [36] would be of first interest.…”

Section: A Related Workmentioning

confidence: 99%

Audio scene monitoring using redundant ad-hoc microphone array networks

Gerstoft,

Hu,

Bianco

et al. 2021

Preprint

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We present a system for localizing sound sources in a room with several microphone arrays. Unlike most existing approaches, the positions of the arrays in space are assumed to be unknown. Each circular array performs direction of arrival (DOA) estimation independently. The DOAs are then fed to a fusion center where they are concatenated and used to perform the localization based on two proposed methods, which require only few labeled source locations for calibration. The first proposed method is based on principal component analysis (PCA) of the observed DOA and does not require any calibration. The array cluster can then perform localization on a manifold defined by the PCA of concatenated DOAs over time. The second proposed method performs localization using an affine transformation between the DOA vectors and the room manifold. The PCA approach has fewer requirements on the training sequence, but is less robust to missing DOAs from one of the arrays. The approach is demonstrated with a set of five 8-microphone circular arrays, placed at unknown fixed locations in an office. Both the PCA approach and the direct approach can easily map out a rectangle based on a few calibration points with similar accuracy as calibration points. The methods demonstrated here provide a step towards monitoring activities in a smart home and require little installation effort as the array locations are not needed.

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Section: A Related Workmentioning

confidence: 99%

Audio scene monitoring using redundant ad-hoc microphone array networks

Gerstoft,

Hu,

Bianco

et al. 2021

Preprint

Self Cite

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“…A manifold learning method for improving RTF estimates based on diffusion maps has been proposed [19,20] and a semisupervised deep learning approach to infer RTFs from source positions has been developed in [21]. A Variational Autoencoder (VAE) has been leveraged for source localization from measured RTFs in [22,23].…”

Section: Introduction and Signal Modelmentioning

confidence: 99%

Manifold learning-supported estimation of relative transfer functions for spatial filtering

Brendel¹,

Zeitler²,

Kellermann³

2021

Preprint

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Many spatial filtering algorithms used for voice capture in, e.g., teleconferencing applications, can benefit from or even rely on knowledge of Relative Transfer Functions (RTFs). Accordingly, many RTF estimators have been proposed which, however, suffer from performance degradation under acoustically adverse conditions or need prior knowledge on the properties of the interfering sources. While state-of-the-art RTF estimators ignore prior knowledge about the acoustic enclosure, audio signal processing algorithms for teleconferencing equipment are often operating in the same or at least a similar acoustic enclosure, e.g., a car or an office, such that training data can be collected. In this contribution, we use such data to train Variational Autoencoders (VAEs) in an unsupervised manner and apply the trained VAEs to enhance imprecise RTF estimates. Furthermore, a hybrid between classic RTF estimation and the trained VAE is investigated. Comprehensive experiments with real-world data confirm the efficacy for the proposed method.

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Semi-supervised source localization in reverberant environments with deep generative modeling

Cited by 2 publications

References 32 publications

Audio scene monitoring using redundant ad-hoc microphone array networks

Audio scene monitoring using redundant ad-hoc microphone array networks

Manifold learning-supported estimation of relative transfer functions for spatial filtering

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