HearFones (HF) have been designed to enhance auditory feedback during phonation. This study investigated the effects of HF (1) on sound perceivable by the subject, (2) on voice quality in reading and singing, and (3) on voice production in speech and singing at the same pitch and sound level.
Test 1: Text reading was recorded with two identical microphones in the ears of a subject. One ear was covered with HF, and the other was free. Four subjects attended this test. Tests 2 and 3: A reading sample was recorded from 13 subjects and a song from 12 subjects without and with HF on. Test 4: Six females repeated [pa:p:a] in speaking and singing modes without and with HF on same pitch and sound level.
Long-term average spectra were made (Tests 1–3), and formant frequencies, fundamental frequency, and sound level were measured (Tests 2 and 3). Subglottic pressure was estimated from oral pressure in [p], and simultaneously electroglottography (EGG) was registered during voicing on [a:] (Test 4). Voice quality in speech and singing was evaluated by three professional voice trainers (Tests 2–4).
HF seemed to enhance sound perceivable at the whole range studied (0–8 kHz), with the greatest enhancement (up to ca 25 dB) being at 1–3 kHz and at 4–7 kHz. The subjects tended to decrease loudness with HF (when sound level was not being monitored). In more than half of the cases, voice quality was evaluated “less strained” and “better controlled” with HF. When pitch and loudness were constant, no clear differences were heard but closed quotient of the EGG signal was higher and the signal more skewed, suggesting a better glottal closure and/or diminished activity of the thyroarytenoid muscle. 相似文献
We derive a canonical model for gradient frequency neural networks (GFNNs) capable of processing time-varying external stimuli. First, we employ normal form theory to derive a fully expanded model of neural oscillation. Next, we generalize from the single oscillator model to heterogeneous frequency networks with an external input. Finally, we define the GFNN and illustrate nonlinear time-frequency transformation of a time-varying external stimulus. This model facilitates the study of nonlinear time-frequency transformation, a topic of critical importance in auditory signal processing. 相似文献