BaNa: A Noise Resilient Fundamental Frequency Detection Algorithm for Speech and Music

Fundamental frequency (F₀) is one of the essential features in many acoustic related applications. Although numerous F₀ detection algorithms have been developed, the detection accuracy in noisy environments still needs improvement. We present a hybrid noise resilient F₀ detection 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 F₀ value among several F₀ candidates. 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 F₀ detection 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.