Automatic Music Classification and Retreival: Experiments with Thai Music Collection

C. Nopthaisong, M. Hasan
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引用次数: 3

Abstract

We present the experimental results of classification and retrieval of Thai music using TreeQ (a tree-structured classifier) and LVQ (Learning Vector Quantization) algorithms in this paper. We use the HTK Toolkit in preprocessing acoustic signals including feature extraction from the Thai music collection. The training set consists of 250 songs -50 songs from each of the 5 genres. Training is divided into three phases using all or some of these songs. The test set consists of 10 songs selected from 5 genres which are not included in training. We trained and tested the music classifiers using both TreeQ and LVQ algorithms with varying parameters such as, Number of Codebook (NOC) and pruning thresholds to identify the effects of different parameters and features in the Thai music classification and retrieval. We observed that TreeQ-based experiments yield faster response-times than those of LVQ; and therefore, a TreeQ-based system maybe appropriate for online (real-time) music retrieval tasks. On the other hand, LVQ-based experiments consistently yield better accuracy than those of TreeQ; and therefore, a LVQ-based system may be appropriate in the music classification task since music classification can generally be performed off-line. We also outlined a Relevance Feedback based Music Retrieval System in this paper.
音乐自动分类与检索:泰国音乐收藏实验
本文介绍了使用树状分类器TreeQ和学习向量量化(LVQ)算法对泰国音乐进行分类和检索的实验结果。我们使用HTK工具箱对声音信号进行预处理,包括从泰国音乐集合中提取特征。训练集由250首歌曲组成-5种流派各50首歌曲。训练分为三个阶段,使用全部或部分歌曲。测试集由10首歌曲组成,这些歌曲从5种流派中选出,不包括在训练中。我们使用TreeQ和LVQ算法训练和测试了具有不同参数的音乐分类器,如代码本数量(NOC)和剪枝阈值,以确定不同参数和特征对泰国音乐分类和检索的影响。我们观察到基于treeq的实验比LVQ的实验产生更快的响应时间;因此,基于treeq的系统可能适合于在线(实时)音乐检索任务。另一方面,基于lvq的实验的准确率始终优于基于TreeQ的实验;因此,基于lvq的系统可能适合于音乐分类任务,因为音乐分类通常可以离线执行。本文还提出了一个基于关联反馈的音乐检索系统。
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