High-fidelity earplugs are used by musicians and live sound engineers to prevent hearing damage while allowing musical sounds to reach the eardrum without distortion. To determine objective methods for judging earplug fidelity in a similar way to headphones or loudspeakers, a small sample of trained listeners were asked to judge the attenuation level and clarity of music through seven commercially available passive earplugs. These scores were then compared to acoustic/musical metrics measured in a laboratory. It was found that NRR is strongly predictive of both attenuation and clarity scores, and that insertion loss flatness provides no advantage over NRR. A different metric measuring spectral flatness distortion seems to predict clarity independently from attenuation and will be subject to further study.
This study investigates listeners’ ability to detect emotion from a diverse set of speech samples, including both spontaneous conversations and actor-posed speech. It explores the contributions of lexical content and acoustic properties when native listeners rate seven pairs of affective attributes. Two experimental conditions were employed: a text condition, where participants evaluated emotional attributes from written transcripts without vocal information, and a voice condition, where participants listened to audio recordings to assess emotions. Results showed that the importance of lexical and vocal cues varies across 14 affective states for posed and spontaneous speech. Vocal cues enhanced the expression of sadness and anger in posed speech, while they had less impact on conveying happiness. Notably, vocal cues tended to mitigate negative emotions conveyed by the lexical content in spontaneous speech. Further analysis on correlations between emotion ratings in text and voice conditions indicated that lexical meanings suggesting anger or hostility could be interpreted as positive affective states like intimacy or confidence. Linear regression analyses indicated that emotional ratings by native listeners could be predicted up to 59% by lexical content and up to 26% by vocal cues. Listeners relied more on vocal cues to perceive emotional tone when the lexical content was ambiguous in terms of feeling and attitude. Finally, the analysis identified statistically significant basic acoustical parameters and other non/para-linguistic information, after controlling for the effect of lexical content.
Controlled listening tests were conducted on five different in-ear (IE) headphone target curves measured on the latest ITU-T Type 4.3 ear simulator (e.g. Bruel & Kjaer Head & Torso Simulator Type 5128). A total of 32 listeners rated each target on a 100-point scale based on preference for three different music programs with two observations each. When averaged across all listeners, two target curves were found to be equally preferred over the other choices. Agglomerative hierarchical clustering analysis further revealed two classes of listeners based on dissimilarities in their preferred target curves. Class One (72% of listeners) preferred the top two rated targets. Class two (28% of listeners) preferred targets with 2 dB less bass and 2 dB more treble than the target curves preferred by Class 1. Among the demographic factors examined, age was the best predictor of membership in each class.
The precedence effect describes our ability to perceive the spatial characteristics of lead and lag sound signals. When the time delay between the lead and lag is sufficiently small we will cease to hear two distinct sounds, instead perceiving the lead and lag as a single fused sound with its own spatial characteristics. Historically, precedence effect models have had difficulty differentiating between lead/lag signals and their fusions. The likelihood of fusion occurring is increased when the signal contains periodicity, such as in the case of music. In this work we present a cepstral analysis based perceptual model of the precedence effect, CEPBIMO, which is more resilient to the presence of fusions than its predecessors. To evaluate our model we employ four datasets of various signal types, each containing 10,000 synthetically generated room impulse responses. The results of the CEPBIMO model are then compared against results of the BICAM. Our results show that the CEPBIMO model is more resilient to the presence of fusions and signal periodicity than previous precedence effect models.
This paper investigates the contextual recognition of neutral thirds in music by integrating real-world musical context into the study of categorical perception. Traditionally, categorical perception has been studied using isolated auditory stimuli in controlled laboratory settings. However, music is typically experienced within a circumstantial framework, significantly influencing its reception. Our study involved musicians from various specializations who listened to precomposed musical fragments, each concluding with a 350-cent interval preceded by different harmonic contexts. The fragments included a monophonic synthesizer and orchestral mockups, with contexts such as major chords, minor chords, a single pitch, neutral thirds, and natural fifths. The results indicate that musical context remarkably affects the recognition of pseudotonal chords. Participants' accuracy in judging interval size varied based on the preceding harmonic context. A statistical analysis was conducted to determine if there were significant differences in the neutral third perception across the different harmonic contexts. The test led to the rejection of the null hypothesis: the findings underscore the need to consider real-world listening experiences in research on auditory processing and cognition.
