A combination of data mining method with decision trees building for Speech/Music discrimination
Nowadays the applications in multimedia domain require that the Speech/Music classifier has many other merits in addition to the accuracy, such as short-time delay and low complexity. Here, we endeavor to form a Speech/Music classifier by using different data mining methods. The main contributions of this paper are to obtain a system by analyzing the inherent validity of diverse features extracted from the audio, building a hierarchical structure of oblique decision trees (HODT) to maintain optimal performances, and applying a novel context-based state transform (ST) strategy to refine the classification results. The proposed algorithm is evaluated by a set of 511min 702 audio files, which are made from 54 speech or music files according to different Signal-to-Noise Ratio (SNR) levels and diverse noise types. The experiment results show that our proposed classifier outperforms AMR-WB+ by achieving 97.9% and 95.9% in classification rate at the 10ms frame level in pure and high SNR (>=20dB) environment, respectively. The post-processing ST strategy further enhances the system performance, particularly at low SNR circumstances (10dB), with 5.6% up in the accuracy rate. In addition, the complexity of the proposed system is lower than 1WMOPS which make it easily adaptable to many scenarios.
real-time discrimination optimal feature subset hierarchical oblique decision tree state transform strategy