Improved Real-Time Monophonic Pitch Tracking with the Extended Complex Kalman Filter

Das O, Smith III JO, Chafe C. Improved Real-Time Monophonic Pitch Tracking with the Extended Complex Kalman Filter. J Audio Eng Soc. 2020;68(1/2):78-86. doi:10.17743/jaes.2019.0053

@article{Das2020_20719,
  author = {Das, Orchisama and Smith III, Julius O. and Chafe, Chris},
  title = {{Improved Real-Time Monophonic Pitch Tracking with the Extended Complex Kalman Filter}},
  journal = {Journal of the Audio Engineering Society},
  volume = {68},
  number = {1/2},
  pages = {78--86},
  year = {2020},
  month = jan,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2019.0053},
  url = {https://doi.org/10.17743/jaes.2019.0053}
}

TY  - JOUR
TI  - Improved Real-Time Monophonic Pitch Tracking with the Extended Complex Kalman Filter
AU  - Das, Orchisama
AU  - Smith III, Julius O.
AU  - Chafe, Chris
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 68
IS  - 1/2
SP  - 78
EP  - 86
PY  - 2020
DA  - 2020/01/06
DO  - 10.17743/jaes.2019.0053
UR  - https://doi.org/10.17743/jaes.2019.0053
PB  - Audio Engineering Society
LA  - en
AB  - This paper proposes a real-time, sample-by-sample pitch tracker for monophonic audio signals using the Extended Kalman Filter in the complex domain, called an Extended Complex Kalman Filter (ECKF). It improves upon the algorithm proposed in a previous paper by fixing the issue of slow tracking of rapid note changes. It does so by detecting harmonic change in the signal, and resetting the filter whenever a significant harmonic change is detected. Along with the fundamental frequency, the ECKF also tracks the amplitude envelope and instantaneous phase of the input audio signal. The pitch tracker is ideal for detecting ornaments in solo instrument music such as slides and vibratos. The improved algorithm is tested to track pitch of bowed string (double-bass), plucked string (guitar), and vocal singing samples. Parameter selection for the ECKF pitch tracker requires knowledge of the type of signal whose pitch is to be tracked, which is a potential drawback. It would be interesting to automatically pick the optimum set of parameters given an audio signal by training on instrument specific datasets.
ER  -