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-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.