Deep learning vector quantization for acoustic information retrieval

We propose a novel deep learning vector quantization (DLVQ) algorithm based on deep neural networks (DNNs). Utilizing a strong representation power of this deep learning framework, with any vector quantization (VQ) method as an initializer, the proposed DLVQ technique is capable of learning a code-c...

Full description

Saved in:
Bibliographic Details
Published in2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 1350 - 1354
Main Authors Zhen Huang, Chao Weng, Kehuang Li, You-Chi Cheng, Chin-Hui Lee
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2014
Subjects
Acoustics
Deep neural network
Information retrieval
k-means
learning vector quantization
Neural networks
Speech
Training
Vector quantization
Vectors
Online AccessGet full text

Cover

More Information
Summary:We propose a novel deep learning vector quantization (DLVQ) algorithm based on deep neural networks (DNNs). Utilizing a strong representation power of this deep learning framework, with any vector quantization (VQ) method as an initializer, the proposed DLVQ technique is capable of learning a code-constrained codebook and thus improves over conventional VQ to be used in classification problems. Tested on an audio information retrieval task, the proposed DLVQ achieves a quite promising performance when it is initialized by the k-means VQ technique. A 10.5% relative gain in mean average precision (MAP) is obtained after fusing the k-means and DLVQ results together.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2014.6853817