Abstract
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.
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Jiang, W., Liu, WJ., Tan, YW., Liang, S. (2014). An Improved Multipitch Tracking Algorithm with Empirical Mode Decomposition. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_22
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DOI: https://doi.org/10.1007/978-3-662-45643-9_22
Publisher Name: Springer, Berlin, Heidelberg
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