Noise robust pitch stylization using minimum mean absolute error criterion
(3 minutes introduction)
|Chiranjeevi Yarra (IIIT Hyderabad, India), Prasanta Kumar Ghosh (Indian Institute of Science, India)|
We propose a pitch stylization technique in the presence of pitch halving and doubling errors. The technique uses an optimization criterion based on a minimum mean absolute error to make the stylization robust to such pitch estimation errors, particularly under noisy conditions. We obtain segments for the stylization automatically using dynamic programming. Experiments are performed at the frame level and the syllable level. At the frame level, the closeness of stylized pitch is analyzed with the ground truth pitch, which is obtained using a laryngograph signal, considering root mean square error (RMSE) measure. At the syllable level, the effectiveness of perceptual relevant embeddings in the stylized pitch is analyzed by estimating syllabic tones and comparing those with manual tone markings using the Levenshtein distance measure. The proposed approach performs better than a minimum mean squared error criterion based pitch stylization scheme at the frame level and a knowledge-based tone estimation scheme at the syllable level under clean and 20dB, 10dB and 0dB SNR conditions with five noises and four pitch estimation techniques. Among all the combinations of SNR, noise and pitch estimation techniques, the highest absolute RMSE and mean distance improvements are found to be 6.49Hz and 0.23, respectively.