Robust Source Counting and DOA Estimation Using Spatial Pseudo-Spectrum and Convolutional Neural Network

Thi Ngoc Tho Nguyen, Woon Seng Gan, Rishabh Ranjan, Douglas L. Jones

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number9178434
Pages (from-to)2626-2637
Number of pages12
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Volume28
DOIs
StatePublished - 2020

Keywords

  • Direction-of-Arrival estimation
  • convolutional neural network
  • multi-Task learning
  • multiple sound sources
  • spatial pseudo-spectrum

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Acoustics and Ultrasonics
  • Computational Mathematics
  • Electrical and Electronic Engineering

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