The spectral cues used for median-plane localization are described by three experiments. First, the frequency spectrum necessary for localization is measured by noting the accuracy of subjects localizing low-and high-pass-filtered white noise. Second, several high-pass, low-pass, bandpass, and bandstop filters are associated with the subjective impression of direction by observing what directions are most frequently perceived by subjects localizing white noise colored by each filter. Third, the frequency responses of several artificial ears are measured for different angles of median-plane sound incidence. Results show that sound spectra from 4 to 16 kHz are necessary for localization. Frontal cues are a 1-octave notch, with a lower-frequency cutoff between 4 and 10 kHz and increased energy above 13 kHz. The "above" cue is a 1/4-octave peak between 7 and 9 kHz, with a high-frequency cutoff at 10 kHz. The "behind" cue is a small peak from 10 to 12 kHz. Increases in frontal elevation are signaled by an increase in the lower cutoff frequency of the 1-octave notch. This notch appears to be generated by time-delayed reflections off the posterior concha wall interfering with sound directly entering the external auditory canal.Subject Classification: 65.62, 65.52.

Several investigators have shown that monaural localization of sound sources on the median plane (MP) is inferior to binaural MP localization, causing speculation that two ears are necessary for MP localization, and further, that two ears may allow binaural processing of asymmetrical pinna filtering making localization of unfamiliar sounds possible. The purpose of the two experiments reported in this paper is (1) to test the hypothesis that binaural subjects can localize unfamiliar sounds more accurately than monaural subjects, and (2) to evaluate monaural localization accuracy after training. The results show that binaural and monaural subjects have similar difficulty in localizing unfamiliar sounds and show that monaural subjects can easily be trained to localize as well as they normally localize with two ears. The results indicate MP localization is fundamentally a monaural process.

Abstract.We consider the function g(z) =f(,[f{t)/t\'xdt for/ in the classes of convex, starlike, and close-to-convex univalent functions, and we determine precisely which values of a yield a closeto-convex function g.

For monaural localization, the time delay between direct and pinna-reflected sound is the dominant feature of sound entering the external ear canal. Experiments measuring the human threshold of delay-time detection and the just noticeable difference of delay time were conducted employing white noise summed with a delay of itself. Threshold results show that delay times of 20 μsec are easily recognizable when the amplitude ratio of the delayed signal to the leading signal is greater than 0.67. Just noticeable difference results agree with measurements of the minimum audible angle for monaural localization. Results further demonstrate correspondence between human detection of a delay-summing process and an equivalent spectral filter.

Background: As adolescent suicide rates continue to rise, innovation in risk identification is warranted. Machine learning can identify suicidal individuals based on their language samples. This feasibility pilot was conducted to explore this technology’s use in adolescent therapy sessions and assess machine learning model performance. Method: Natural language processing machine learning models to identify level of suicide risk using a smartphone app were tested in outpatient therapy sessions. Data collection included language samples, depression and suicidality standardized scale scores, and therapist impression of the client’s mental state. Previously developed models were used to predict suicidal risk. Results: 267 interviews were collected from 60 students in eight schools by ten therapists, with 29 students indicating suicide or self-harm risk. During external validation, models were trained on suicidal speech samples collected from two separate studies. We found that support vector machines (AUC: 0.75; 95% CI: 0.69–0.81) and logistic regression (AUC: 0.76; 95% CI: 0.70–0.82) lead to good discriminative ability, with an extreme gradient boosting model performing the best (AUC: 0.78; 95% CI: 0.72–0.84). Conclusion: Voice collection technology and associated procedures can be integrated into mental health therapists’ workflow. Collected language samples could be classified with good discrimination using machine learning methods.