Mapping Sounds on Images Using Binaural Spectrograms

Antoine Deleforge 1, * Vincent Drouard 1 Laurent Girin 2 Radu Horaud 1
* Corresponding author
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 GIPSA-MAGIC - MAGIC
GIPSA-DPC - Département Parole et Cognition
Abstract : We propose a novel method for mapping sound spectrograms onto images and thus enabling alignment between auditory and visual features for subsequent multimodal processing. We suggest a supervised learning approach to this audio-visual fusion problem, on the following grounds. Firstly, we use a Gaussian mixture of locally-linear regressions to learn a mapping from image locations to binaural spectrograms. Secondly, we derive a closed-form expression for the conditional posterior probability of an image location, given both an observed spectrogram, emitted from an unknown source direction, and the mapping parameters that were previously learnt. Prominently, the proposed method is able to deal with completely different spectrograms for training and for alignment. While fixed-length wide-spectrum sounds are used for learning, thus fully and robustly estimating the regression, variable-length sparse-spectrum sounds, e.g., speech, are used for alignment. The proposed method successfully extracts the image location of speech utterances in realistic reverberant-room scenarios.
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Antoine Deleforge, Vincent Drouard, Laurent Girin, Radu Horaud. Mapping Sounds on Images Using Binaural Spectrograms. 22nd European Signal Processing Conference (EUSIPCO-2014), Sep 2014, Lisbonne, Portugal. pp.2470 - 2474. ⟨hal-01019287⟩

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