Next Article in Journal
Measurement of the X-ray Spectrum of a Free Electron Laser with a Wide-Range High-Resolution Single-Shot Spectrometer
Next Article in Special Issue
Spatial Audio for Soundscape Design: Recording and Reproduction
Previous Article in Journal
A Study on Evaluation and Application of Snowmelt Performance of Anti-Icing Asphalt Pavement
Previous Article in Special Issue
Low Frequency Interactive Auralization Based on a Plane Wave Expansion
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessFeature PaperArticle
Appl. Sci. 2017, 7(6), 582; doi:10.3390/app7060582

Solution Strategies for Linear Inverse Problems in Spatial Audio Signal Processing

1
Department of Power Mechanical Engineering, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu 30013, Taiwan
2
Department of Electrical Engineering, National Chiao Tung University, No. 1001, Ta-Hsueh Road, Hsinchu 30013, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Woon Seng Gan and Jung-Woo Choi
Received: 30 March 2017 / Revised: 15 May 2017 / Accepted: 26 May 2017 / Published: 5 June 2017
(This article belongs to the Special Issue Spatial Audio)

Abstract

The aim of this study was to compare algorithms for solving inverse problems generally encountered in spatial audio signal processing. Tikhonov regularization is typically utilized to solve overdetermined linear systems in which the regularization parameter is selected by the golden section search (GSS) algorithm. For underdetermined problems with sparse solutions, several iterative compressive sampling (CS) methods are suggested as alternatives to traditional convex optimization (CVX) methods that are computationally expensive. The focal underdetermined system solver (FOCUSS), the steepest descent (SD) method, Newton’s (NT) method, and the conjugate gradient (CG) method were developed to solve CS problems more efficiently in this study. These algorithms were compared in terms of problems, including source localization and separation, noise source identification, and analysis and synthesis of sound fields, by using a uniform linear array (ULA), a uniform circular array (UCA), and a random array. The derived results are discussed herein and guidelines for the application of these algorithms are summarized. View Full-Text
Keywords: inverse problems; Tikhonov regularization; compressive sensing (CS); convex optimization (CVX); focal underdetermined system solver (FOCUSS); steepest descent (SD); Newton’s method (NT); conjugate gradient (CG); golden section search (GSS) inverse problems; Tikhonov regularization; compressive sensing (CS); convex optimization (CVX); focal underdetermined system solver (FOCUSS); steepest descent (SD); Newton’s method (NT); conjugate gradient (CG); golden section search (GSS)
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Bai, M.R.; Chung, C.; Wu, P.-C.; Chiang, Y.-H.; Yang, C.-M. Solution Strategies for Linear Inverse Problems in Spatial Audio Signal Processing. Appl. Sci. 2017, 7, 582.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top