Objectives: The objective of this study was to evaluate the influence of music versus speech on a listener's signal-to-noise ratio (SNR) preferences when listening in a noise background. SNR preferences were quantified using acceptable noise level (ANL) and preferred SNR metrics. The measurement paradigm for ANL allows the listener to adjust the level of background noise while listening to the target at their most comfortable loudness level. A higher ANL indicates less tolerance for noise and a lower ANL indicates high tolerance for noise. The preferred SNR is simply the SNR the listener prefers when attending to a target in a fixed-amount (level) of background noise. In contrast to the ANL, the listener does not have control over the noise. Rather, they are only able to manipulate the target level. The first aim of the study was to determine if listeners' tolerances for noise, quantified using the ANL, when listening to music is different from that when listening to speech. The second aim of the study was to determine if listeners' tolerances for noise, quantified using their preferred SNR, when listening to music is different from that when listening to speech. The third aim of the study was to quantify the relationship between ANL and preferred SNR.
Design: Ninety-nine normal-hearing, native-English speakers participated in this study. The ANL and preferred SNR were measured for speech and music targets. Music targets included two variations (with lyrics and without lyrics) of the song "Rocky Top." Measurements were made in the sound field at 0° azimuth, 1.5 m from a loud speaker. For both ANL and preferred SNR, targets were presented in 12-talker babble noise. The level of the noise was adjusted by the listener during ANL measurement but was fixed in level during the preferred SNR measurement (75 dB A). Repeated-measures analysis of variance was performed to identify any significant effect of target on the ANL and preferred SNR. Correlation analysis was performed to evaluate the relationship between ANL and preferred SNR.
Results: Findings demonstrate a significant effect of target on ANL and preferred SNR. ANLs were highest for speech (mean = 7.2 dB), followed by music with lyrics (6.1 dB), and music without lyrics (2.5 dB). Preferred SNRs were highest for music with lyrics (mean = 2.3 dB), followed by speech (1.2 dB), and music without lyrics (-0.1 dB). A listener's ANL for a given target was strongly correlated with their ANL for a different target (the same was true for preferred SNR); however, ANL for a given target was not a statistically significant predictor of preferred SNR for the same target.
Conclusions: When listening in a background of noise, the listener's tolerance for noise depends on the target to which they are attending, whether music or speech. This dependence is especially evident for ANL measures, and less so for preferred SNR measures. Despite differences in ANL and preferred SNR across targets, a listener's ANL and preferred SNR for one target predicts their ANL and preferred SNR, respectively, for a different target. The lack of correlation between ANL and preferred SNR suggests different mechanisms underly these listener-preference metrics.
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http://dx.doi.org/10.1097/AUD.0000000000001157 | DOI Listing |
Sensors (Basel)
December 2024
Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology in Szczecin, al. Piastow 17, 70-310 Szczecin, Poland.
The safety of the airspace could be improved by the use of visual methods for the detection and tracking of aircraft. However, in the case of the small angular size of airplanes and the high noise level in the image, sufficient use of such methods might be difficult. By using the ConvNN (Convolutional Neural Network), it is possible to obtain a detector that performs the segmentation task for aircraft images that are very small and lost in the background noise.
View Article and Find Full Text PDFPurpose: With the widespread introduction of dual energy computed tomography (DECT), applications utilizing the spectral information to perform material decomposition became available. Among these, a popular application is to decompose contrast-enhanced CT images into virtual non-contrast (VNC) or virtual non-iodine images and into iodine maps. In 2021, photon-counting CT (PCCT) was introduced, which is another spectral CT modality.
View Article and Find Full Text PDFKorean J Radiol
January 2025
Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Objective: The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2) and a conventional T2-w FSE Dixon sequence (T2) for breast magnetic resonance imaging (MRI).
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Acta Radiol
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View Article and Find Full Text PDFEur J Radiol Open
December 2024
Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls-University Tuebingen, Tuebingen D-72076, Germany.
Unlabelled: Diagnostic accuracy and therapeutic decision-making for IDH-mutant gliomas in tumor board reviews are based on MRI and multidisciplinary interactions.
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