In this paper we propose a method of extending monaural into stereophonic sound based on deep neural networks (DNNs). First, it is assumed that monaural signals are the mid signals for the extended stereo signals. In addition, the residual signals are obtained by performing the linear prediction (LP) analysis. The LP coefficients of monaural signals are converted into the line spectral frequency (LSF) coefficients. After that, the LSF coefficients are taken as the DNN features, and the features of the side signals are estimated from those of the mid signals. The performance of the proposed method is evaluated using a log spectral distortion (LSD) measure and a multiple stimuli with a hidden reference and anchor (MUSHRA) test. It is shown from the performance comparison that the proposed method provides lower LSD and higher MUSHRA score than a conventional method using hidden Markov model (HMM).

Authors: Chun, Chan Jun; Jeong, Seok Hee; Park, Su Yeon; Kim, Hong Kook
Affiliations: Gwangju Institute of Science and Technology (GIST), Gwangju, Korea; City University of New York, New York, NY, USA(See document for exact affiliation information.)
AES Convention: 139 (October 2015) Paper Number: 9400
Publication Date: October 23, 2015 Import into BibTeX
Subject: Signal Processing
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=17957

Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!

This paper costs $33 for non-members and is free for AES members and E-Library subscribers.

Learn more about the AES E-Library

E-Library Location: (CD 139Papers)   /conv/139/9400.pdf

Start a discussion about this paper!