The Journal of the Acoustical Society of America · 2008 · 152 citations · 18 references
EngineeringAudiologyAcousticsPhoneticsPsychoacousticsAcoustic TrackingDigital Signal ProcessingFrequency-domain AnalysisSignal ProcessingSpeech RecognitionTime-frequency AnalysisAutocorrelation FunctionFrequency Domain ProcessingNoise Robust Speech AnalysisNormalized Cross-correlationPeak PickingPitch TrackingSpectral Analysis Of SpeechTelephone Speech Analysis
The paper introduces a robust fundamental‑frequency tracking algorithm that performs well on both high‑quality and telephone speech across a wide range of signal‑to‑noise ratios. The algorithm, named YAAPT, combines time‑domain normalized cross‑correlation with frequency‑domain processing, employs a modified autocorrelation that incorporates multiple spectral harmonic peaks, selects multiple F0 candidates with associated figures of merit, and uses dynamic programming to choose the best track. On clean speech the method attains error rates comparable to the best existing algorithms, and on noisy telephone speech it achieves lower error rates than other published methods.
In this paper, a fundamental frequency (F0) tracking algorithm is presented that is extremely robust for both high quality and telephone speech, at signal to noise ratios ranging from clean speech to very noisy speech. The algorithm is named “YAAPT,” for “yet another algorithm for pitch tracking.” The algorithm is based on a combination of time domain processing, using the normalized cross correlation, and frequency domain processing. Major steps include processing of the original acoustic signal and a nonlinearly processed version of the signal, the use of a new method for computing a modified autocorrelation function that incorporates information from multiple spectral harmonic peaks, peak picking to select multiple F0 candidates and associated figures of merit, and extensive use of dynamic programming to find the “best” track among the multiple F0 candidates. The algorithm was evaluated by using three databases and compared to three other published F0 tracking algorithms by using both high quality and telephone speech for various noise conditions. For clean speech, the error rates obtained are comparable to those obtained with the best results reported for any other algorithm; for noisy telephone speech, the error rates obtained are lower than those obtained with other methods.
18