An Improved Multipitch Tracking Algorithm with Empirical Mode Decomposition
Multipitch tracking is beneficial for speech separation,audio transcription and many other tasks.In this paper,we greatly improve a state-of-the-art multipitch tracking algorithm.While the amplitude and individual peak positions of autocorrelation function (ACF) were used in previous algorithms,a novel feature based on the average frequency of each time-frequency (T-F) unit is proposed in this paper.This feature is computed by an empirical mode decomposition (EMD) method.Then it is utilized to form the conditional probabilities in the hidden Markov model (HMM) given a pitch state of each frame,and finally the most likely state sequence is searched out.Quantitative evaluations show that the novel feature is more effective,and our algorithm significantly outperforms the previous one.
multipitch determination algorithm empirical mode decomposition instantaneous frequency HMM tracking
Wei Jiang Wenju Liu Yingwei Tan Shan Liang
NLPR,Institute of Automation,Chinese Academy of Sciences,Beijing,China
国际会议
Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)
长沙
英文
209-217
2014-11-01（万方平台首次上网日期，不代表论文的发表时间）