Time-efficient measures of auditory frequency selectivity

被引:11
|
作者
Charaziak, Karolina K. [1 ]
Souza, Pamela [1 ]
Siegel, Jonathan H. [1 ]
机构
[1] Northwestern Univ, Dept Commun Sci & Disorders, Sch Commun, Evanston, IL 60208 USA
关键词
Psychophysical tuning curves; Frequency selectivity; Equivalent rectangular bandwidth; Normally-hearing; PSYCHOPHYSICAL TUNING CURVES; FILTER SHAPES; DEAD REGIONS; HEARING-LOSS; SIMULTANEOUS MASKING; NORMALLY-HEARING; NOISE; CHILDREN; MASKERS; SPEECH;
D O I
10.3109/14992027.2011.625982
中图分类号
R36 [病理学]; R76 [耳鼻咽喉科学];
学科分类号
100104 ; 100213 ;
摘要
Objective: The objective of this study was to compare two recently proposed methods for fast measurements of psychophysical tuning curves (fast-PTCs) in terms of resulting tuning curve features and training effects. Design: Fast-PTCs with swept-noise (SN) and gated-noise (GN) maskers were measured at signal frequencies of 500, 1000, 2000, and 4000 Hz. The effect of amplitude modulating the signal in the GN condition was evaluated. Two PTC runs were obtained for each condition to assess training effects. Study sample: Eight normally-hearing young adults participated in the study. Results: The SN and GN methods resulted in similar estimates of frequency selectivity when training effects were considered. Amplitude modulating the tone in the GN method reduced the effect of training. On average, SN-PTCs were most repeatable compared to the two other methods and they were not affected by training. Estimation of the shift in the PTC tip frequency was not affected by the measurement method or training effects. Fast-PTC methods resulted in similar estimates of tuning as compared to published notched-noise data. Conclusions: The SN method and the GN procedure with amplitude modulated signals allowed for time-efficient estimation of frequency selectivity that was unaffected by training.
引用
收藏
页码:317 / 325
页数:9
相关论文
共 50 条
  • [31] Time-Efficient A* Algorithm for Robot Path Planning
    Guruji, Akshay Kumar
    Agarwal, Himansh
    Parsediya, D. K.
    3RD INTERNATIONAL CONFERENCE ON INNOVATIONS IN AUTOMATION AND MECHATRONICS ENGINEERING 2016, ICIAME 2016, 2016, 23 : 144 - 149
  • [32] Frequency Selectivity in the Bottlenose Dolphin Auditory System
    Zaslavski, Gennadi
    EFFECTS OF NOISE ON AQUATIC LIFE, 2012, 730 : 41 - 43
  • [34] Time-Efficient Convolutional Neural Network-Assisted Brillouin Optical Frequency Domain Analysis
    Karapanagiotis, Christos
    Wosniok, Aleksander
    Hicke, Konstantin
    Krebber, Katerina
    SENSORS, 2021, 21 (08)
  • [35] Auditory system: A neural substrate for frequency selectivity?
    King, AJ
    CURRENT BIOLOGY, 1998, 8 (01) : R25 - R27
  • [36] CENTRAL FACTORS IN AUDITORY FREQUENCY-SELECTIVITY
    SWETS, JA
    PSYCHOLOGICAL BULLETIN, 1963, 60 (05) : 429 - 440
  • [37] Space- and Time-Efficient Polynomial Multiplication
    Roche, Daniel S.
    ISSAC2009: PROCEEDINGS OF THE 2009 INTERNATIONAL SYMPOSIUM ON SYMBOLIC AND ALGEBRAIC COMPUTATION, 2009, : 295 - 301
  • [38] Time-efficient model checking with magnetic disk
    Bao, T
    Jones, M
    TOOLS AND ALGORITHMS FOR THE CONSTRUCTION AND ANALYSIS OF SYSTEMS, PROCEEDINGS, 2005, 3440 : 526 - 540
  • [39] Reliable and Time-efficient Virtualized Function Placement
    Ben Haim, Roi
    Rottenstreich, Ori
    2020 IEEE SYMPOSIUM ON HIGH-PERFORMANCE INTERCONNECTS (HOTI 2020), 2020, : 71 - 78
  • [40] Rapid evolution of time-efficient packet classifiers
    Salomon, Ralf
    Widiger, Harald
    Tockhorn, Andreas
    2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6, 2006, : 2778 - +