Tone-in-Noise Detection Using Envelope Cues: Comparison of Signal-Processing-Based and Physiological Models

被引:17
|
作者
Mao, Junwen [1 ]
Carney, Laurel H. [2 ,3 ]
机构
[1] Univ Rochester, Dept Elect & Comp Engn, Rochester, NY USA
[2] Univ Rochester, Dept Biomed Engn, Rochester, NY 14627 USA
[3] Univ Rochester, Dept Neurobiol & Anat, Rochester, NY USA
关键词
tone-in-noise detection; envelope; physiological model; AMPLITUDE-MODULATED TONES; ANTEROVENTRAL COCHLEAR NUCLEUS; MASKING-LEVEL DIFFERENCES; POWER-LAW DYNAMICS; REPRODUCIBLE NOISE; NARROW-BAND; BINAURAL DETECTION; PHENOMENOLOGICAL MODEL; INFERIOR COLLICULUS; AUDITORY PERIPHERY;
D O I
10.1007/s10162-014-0489-1
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Tone-in-noise detection tasks with reproducible noise maskers have been used to identify cues that listeners use to detect signals in noisy environments. Previous studies have shown that energy, envelope, and fine-structure cues are significantly correlated to listeners' performance for detection of a 500-Hz tone in noise. In this study, envelope cues were examined for both diotic and dichotic tone-in-noise detection using both stimulus-based signal processing and physiological models. For stimulus-based envelope cues, a modified envelope slope model was used for the diotic condition and the binaural slope of the interaural envelope difference model for the dichotic condition. Stimulus-based models do not include key nonlinear transformations in the auditory periphery such as compression, rate and dynamic range adaptation, and rate saturation, all of which affect the encoding of the stimulus envelope. For physiological envelope cues, stimuli were passed through models for the auditory nerve (AN), cochlear nucleus, and inferior colliculus (IC). The AN and cochlear nucleus models included appropriate modulation gain, another transformation of the stimulus envelope that is not typically included in stimulus-based models. A model IC cell was simulated with a linear band-pass modulation filter. The average discharge rate and response fluctuations of the model IC cell were compared to human performance. Previous studies have predicted a significant amount of the variance across reproducible noise maskers in listeners' detection using stimulus-based envelope cues. In this study, a physiological model that includes neural mechanisms that affect encoding of the stimulus envelope predicts a similar amount of the variance in listeners' performance across noise maskers.
引用
收藏
页码:121 / 133
页数:13
相关论文
共 50 条
  • [11] Underwater Noise Signal Processing Method Based on LMD Envelope Spectrum
    Lu, Tao
    Zheng, Yi
    Mudugamuwa, Amith
    2018 IEEE 18TH INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY (ICCT), 2018, : 1056 - 1062
  • [12] Machine Learning-based Signal Processing Using Physiological Signals for Stress Detection
    Ghaderi, Adnan
    Frounchi, Javad
    Farnam, Alireza
    2015 22ND IRANIAN CONFERENCE ON BIOMEDICAL ENGINEERING (ICBME), 2015, : 93 - 98
  • [13] Emotion Detection Using Psycho-Physiological Signal Processing
    Safta, Ioana
    Grigore, Ovidiu
    Caruntu, Constantin
    2011 7TH INTERNATIONAL SYMPOSIUM ON ADVANCED TOPICS IN ELECTRICAL ENGINEERING (ATEE), 2011,
  • [14] A signal-processing-based technique for P300 evoked potential detection with the applications into automated character recognition
    Chen, Szi-Wen
    Lai, Yeh-Chi
    EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2014, : 1 - 10
  • [15] A signal-processing-based technique for P300 evoked potential detection with the applications into automated character recognition
    Szi-Wen Chen
    Yeh-Chi Lai
    EURASIP Journal on Advances in Signal Processing, 2014
  • [16] Impact of Label Noise on the Learning Based Models for a Binary Classification of Physiological Signal
    Ding, Cheng
    Pereira, Tania
    Xiao, Ran
    Lee, Randall J.
    Hu, Xiao
    SENSORS, 2022, 22 (19)
  • [17] UNKNOWN SIGNAL DETECTION IN INTERFERENCE AND NOISE USING HIDDEN MARKOV MODELS
    Ford, Gabriel
    Foster, Benjamin J.
    Liston, Michael J.
    Kam, Moshe
    2021 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP), 2021, : 406 - 410
  • [18] Analysis of Barkhausen noise using wavelet-based fractal signal processing for fatigue crack detection
    Miesowicz, Krzysztof
    Staszewski, Wieslaw J.
    Korbiel, Tomasz
    INTERNATIONAL JOURNAL OF FATIGUE, 2016, 83 : 109 - 116
  • [19] Audio signal based danger detection using signal processing and deep learning
    Fime, Awal Ahmed
    Ashikuzzaman, Md.
    Aziz, Abdul
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 237
  • [20] Stress detection in computer users based on digital signal processing of noninvasive physiological variables
    Zhai, Jing
    Barreto, Armando
    2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15, 2006, : 1999 - +