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Robust Source Counting and DOA Estimation Using Spatial Pseudo-Spectrum and Convolutional Neural Network
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DOI：10.1109/TASLP.2020.3019646.png)
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摘要
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Many signal processing-based methods for sound source direction-of-arrival estimation produce a spatial pseudo-spectrum of which the local maxima strongly indicate the source directions. Due to different levels of noise, reverberation and different number of overlapping sources, the spatial pseudo-spectra are noisy even after smoothing. In addition, the number of sources is often unknown. As a result, selecting the peaks from these spectra is susceptible to error. Convolutional neural network has been successfully applied to many image processing problems in general and direction-of-arrival estimation in particular. In addition, deep learning-based methods for direction-of-arrival estimation show good generalization to different environments. We propose to use a 2D convolutional neural network with multi-task learning to robustly estimate the number of sources and the directions-of-arrival from short-time spatial pseudo-spectra, which have useful directional information from audio input signals. This approach reduces the tendency of the neural network to learn unwanted association between sound classes and directional information, and helps the network generalize to unseen sound classes. The simulation and experimental results show that the proposed methods outperform other directional-of-arrival estimation methods in different levels of noise and reverberation, and different number of sources.
Keyword:
Direction-of-arrival estimation
Estimation
Two dimensional displays
Reverberation
Speech processing
Robustness
Convolutional neural networks
Direction-of-arrival estimation
convolutional neural network
spatial pseudo-spectrum
multi-task learning
multiple sound sources
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