Two-Microphone End-to-End Speaker Joint Identification and Localization Via Convolutional Neural Networks

被引：1
|
作者：
Salvati, Daniele [1 ]
Drioli, Carlo [1 ]
Foresti, Gian Luca [1 ]
机构：
[1] Univ Udine, Dept Math Comp Sci & Phys, Udine, Italy
来源：
2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2020年
关键词：
Convolutional neural network; end-to-end system; raw waveform; speaker identification; speaker localization; two-microphone array; ACOUSTIC SOURCE LOCALIZATION; NOISY;
D O I：
10.1109/ijcnn48605.2020.9206674
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We present an end-to-end scheme based on convolutional neural networks (CNNs) for speaker joint identification and localization. We investigate the possibility to estimate both the direction of arrival (DOA) and the identity of the speaker in far-field noisy and reverberant conditions using a two-channel microphone array. The proposed CNN network is designed to map the raw waveform of the two channels into the speaker identity and into the DOA of its speech signal. We analyze the identification and localization performance with simulated experiments in noisy and reverberation conditions.
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页数：6
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