CRNN-based joint azimuth and elevation localization with the Ambisonics intensity vector

Lauréline Perotin 1, 2 Romain Serizel 2 Emmanuel Vincent 2 Alexandre Guérin 1
1 Orange Labs [Cesson-Sévigné]
2 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : We present a source localization system for first-order Ambisonics (FOA) contents based on a stacked convolutional and recurrent neural network (CRNN). We propose to use as input to the CRNN the FOA acoustic intensity vector, which is easy to compute and closely linked to the sound direction of arrival (DoA). The system estimates the DoA of a point source in both azimuth and elevation. We conduct an experimental evaluation in configurations including reverberation, noise, and various speaker w.r.t. microphone orientations. The results show that the proposed architecture and input allow the network to return accurate location estimates in realistic conditions compared to another recent CRNN-based system.
Keywords : acoustic intensity CRNN direction of arrival first-order Ambisonics (FOA)
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Lauréline Perotin, Romain Serizel, Emmanuel Vincent, Alexandre Guérin. CRNN-based joint azimuth and elevation localization with the Ambisonics intensity vector. IWAENC 2018 - 16th International Workshop on Acoustic Signal Enhancement, Sep 2018, Tokyo, Japan. ⟨hal-01840453⟩

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