Abstract
A source signal is estimated using an associative memory model (AMM) and used for separation matrix optimization in linear blind source separation (BSS) to yield high quality and less distorted speech. Linear-filtering-based BSS, such as independent vector analysis (IVA), has been shown to be effective in sound source separation while avoiding non-linear signal distortion. This technique, however, requires several assumptions of sound sources being independent and generated from non-Gaussian distribution. We propose a method for estimating a linear separation matrix without any assumptions about the sources by repeating the following two steps: estimating non-distorted reference signals by using an AMM and optimizing the separation matrix to minimize an error between the estimated signal and reference signal. Experimental comparisons carried out in simultaneous speech separation suggest that the proposed method can reduce the residual distortion caused by IVA.
Original language | English |
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Title of host publication | 2015 23rd European Signal Processing Conference, EUSIPCO 2015 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1098-1102 |
Number of pages | 5 |
ISBN (Print) | 9780992862633 |
DOIs | |
Publication status | Published - 2015 Dec 22 |
Event | 23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France Duration: 2015 Aug 31 → 2015 Sep 4 |
Other
Other | 23rd European Signal Processing Conference, EUSIPCO 2015 |
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Country | France |
City | Nice |
Period | 15/8/31 → 15/9/4 |
Keywords
- convolutional neural network
- denoising autoencoder associative memory model linear filtering blind
ASJC Scopus subject areas
- Media Technology
- Computer Vision and Pattern Recognition
- Signal Processing