Fully convolutional networks trained on synthesized subharmonic phonation classify subharmonic period M=1-4 with >98% synthetic accuracy, with qualitative only evidence on real voices.
Comparison of fundamental frequency estimators with subharmonic voice signals
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abstract
In clinical voice signal analysis, mishandling of subharmonic voicing may cause an acoustic parameter to signal false negatives. As such, the ability of a fundamental frequency estimator to identify speaking fundamental frequency is critical. This paper presents a sustained-vowel study, which used a quality-of-estimate classification to identify subharmonic errors and subharmonics-to-harmonics ratio (SHR) to measure the strength of subharmonic voicing. Five estimators were studied with a sustained vowel dataset: Praat, YAAPT, Harvest, CREPE, and FCN-F0. FCN-F0, a deep-learning model, performed the best both in overall accuracy and in correctly resolving subharmonic signals. CREPE and Harvest are also highly capable estimators for sustained vowel analysis.
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Towards detecting the pathological subharmonic voicing with fully convolutional neural networks
Fully convolutional networks trained on synthesized subharmonic phonation classify subharmonic period M=1-4 with >98% synthetic accuracy, with qualitative only evidence on real voices.