BaNa: A Noise Resilient Fundamental Frequency Detection Algorithm for Speech and Music
Na Yang, He Ba, Weiyang Cai, Ilker Demirkol, Wendi Heinzelman · IEEE/ACM Transactions on Audio Speech and Language Processing · 2014
Fundamental frequency (F0) is one of the essential features in many acoustic related applications. Although numerous F0detection algorithms have been developed, the detection accuracy in noisy environments still needs improvement. We present a hybrid noise resilient F0detection algorithm named BaNa that combines the approaches of harmonic ratios and Cepstrum analysis. A Viterbi algorithm with a cost function is used to identify the F0value among several F0candidates. Speech and music databases with eight different types of additive noise are used to evaluate the performance of the BaNa algorithm and several classic and state-of-the-art F0detection algorithms. Results show that for almost all types of noise and signal-to-noise ratio (SNR) values investigated, BaNa achieves the lowest Gross Pitch Error (GPE) rate among all the algorithms. Moreover, for the 0 dB SNR scenarios, the BaNa algorithm is shown to achieve 20% to 35% GPE rate for speech and 12% to 39% GPE rate for music. We also describe implementation issues that must be addressed to run the BaNa algorithm as a real-time application on a smartphone platform.