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|Title:||Robust source counting and DOA estimation using spatial pseudo-spectrum and convolutional neural network||Authors:||Nguyen, Thi Ngoc Tho
Jones, Douglas L.
|Keywords:||Engineering||Issue Date:||2020||Source:||Nguyen, T. N. T., Gan, W.-S., Ranjan R., & Jones, D. L. (2020). Robust source counting and DOA estimation using spatial pseudo-spectrum and convolutional neural network. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 2626-2637. doi: 10.1109/TASLP.2020.3019646||Journal:||IEEE/ACM Transactions on Audio, Speech, and Language Processing||Abstract:||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.||URI:||https://hdl.handle.net/10356/144539||ISSN:||2329-9304||DOI:||10.1109/TASLP.2020.3019646||Rights:||© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TASLP.2020.3019646||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
Updated on Jan 27, 2022
Updated on Jan 27, 2022
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