Name
Abstract | ||
|---|---|---|
High-quality content-based music similarity retrieval methods are non-vectorial and use non-metric divergence measures, which prevents the expansion of music recommendation systems. We presents a GPU-based method to speed up content-based music similarity search in large-scale collections, in order to improve the response speed without reducing retrieval accuracy. The method also introduce an optimization technique based on memory layout to improve memory access. The efficiency of our method is validated through extensive experiments. Evaluation results show that our single GPU implementation achieves 10x speedup ratio on NVIDIA GTX480, when compared to a typical general purpose CPU's execution time. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/GreenCom-iThings-CPSCom.2013.341 | GreenCom/iThings/CPScom |
Keywords | Field | DocType |
optimisation,large-scale music similarity retrieval,music recommendation,gpu-based method,nvidia gtx480,content-based music similarity search,gpu,retrieval accuracy,music,high-quality content-based music similarity,information retrieval,memory access,graphics processing units,recommender systems,cuda,music recommendation system,memory layout,gpu-accelerated large-scale music similarity,retrieval method,nonmetric divergence measures,content-based retrieval,music similarity retrieval,high-quality content-based music similarity retrieval,response speed,optimization technique | Recommender system,Information retrieval,General purpose,CUDA,Computer science,Execution time,Content based retrieval,Nearest neighbor search,Speedup | Conference |
Citations | PageRank | References |
1 | 0.36 | 7 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Limin Xiao | 1 | 231 | 47.05 |
| Yao Zheng | 2 | 3 | 2.10 |
| Wenqi Tang | 3 | 3 | 1.42 |
| Guangchao Yao | 4 | 4 | 2.13 |
| Li Ruan | 5 | 123 | 25.10 |
| Xiang Wang | 6 | 26 | 15.33 |