Digital filter simulation of the basilar membrane

Ambikairajah, Eliathamby; Black, Norman D.; Linggard, R.

doi:10.1016/0885-2308(89)90024-7

Cited by 17 publications

(5 citation statements)

References 5 publications

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“…A one-dimensional computational passive cochlear model was proposed in [18], where each section of the basilar membrane (BM) was modelled using a filter section consisting of lowpass, resonant and notch filters connected in series. The input to each filter section is the pressure (s), and the output pressure ( ) is transferred to next filter section.…”

Section: Background: Passive Transmission Line Cochlear Modelmentioning

confidence: 99%

“…This way the pressure wave will travel along the BM from the high frequency end to the low frequency end. The pressure transfer function of a given filter section is [18] ( )…”

Section: Background: Passive Transmission Line Cochlear Modelmentioning

confidence: 99%

“…The pressure transfer function (Eq. (1)) contains the displacement transfer function as well [18], consequently the displacement of the basilar membrane was obtained from each filter section using only the displacement transfer function (poles only) [18].…”

Section: Background: Passive Transmission Line Cochlear Modelmentioning

confidence: 99%

“…The block diagram of the proposed modified model is shown in Figure 1. The pressure along the basilar membrane is modelled in the same way as in [18]. The BM displacement is modelled by converting pressure into displacement by adding a lowpass filter ( ( )) in parallel as shown in Figure 1.…”

Section: Proposed Modified Cochlear Modelmentioning

confidence: 99%

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An Adaptive-Q Cochlear Model for Replay Spoofing Detection

et al. 2019

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Replay attack poses a key threat for automatic speaker verification systems. Spoofing detection systems inspired by auditory perception have shown promise to date, however some aspects of auditory processing have not been investigated in this context. In this paper, a transmission line cochlear model that incorporates an active feedback mechanism is proposed for replay attack detection. This model compresses the considerable energy variation in each auditory sub-band filter by boosting low-amplitude signal, an effect that is not considered in many auditory models. To perform the compression, the parameters of each auditory sub-band filter are modified based on the sub-band energy, analogous to the effect of the closed-loop adaptation mechanism that allows perception of a wide dynamic range from a physically constrained system, which we term adaptive-Q. Evaluation on the ASVspoof 2017 version 2 database suggests that the adaptive-Q compression provided by the proposed model helps to improve the performance of replay detection, and a relative reduction in EER of 26% was achieved compared with the best results reported for auditory system-based feature proposed for replay attack detection.

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Section: Background: Passive Transmission Line Cochlear Modelmentioning

confidence: 99%

“…This way the pressure wave will travel along the BM from the high frequency end to the low frequency end. The pressure transfer function of a given filter section is [18] ( )…”

Section: Background: Passive Transmission Line Cochlear Modelmentioning

confidence: 99%

Section: Background: Passive Transmission Line Cochlear Modelmentioning

confidence: 99%

Section: Proposed Modified Cochlear Modelmentioning

confidence: 99%

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An Adaptive-Q Cochlear Model for Replay Spoofing Detection

et al. 2019

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“…Based on such physiological evidence, many computational and analytical models to replicate the active and passive behavior of cochlea have been developed [21][22][23][24]. Passive behavior is typically modelled as a cascaded bank of filters tuned to different frequencies [25]. Different active mechanisms have been incorporated into the passive models, making the overall filterbank behavior adaptive in nature [22].…”

Section: Introductionmentioning

confidence: 99%

Biologically Inspired Adaptive-Q Filterbanks for Replay Spoofing Attack Detection

2019

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Development of generalizable countermeasures for replay spoofing attacks on Automatic Speaker Verification (ASV) systems is still an open problem. Many countermeasures to date utilize bandpass filters to extract a variety of frequency band-based features. This paper proposes the use of adaptive bandpass filters, a concept adopted from human cochlear modelling to improve detection performance. Gains of filters used for subband based feature extraction are adaptively adjusted by varying their Q factors (Quality factor) as a function of input signal level to boost low amplitude signal components and improve the front-end's sensitivity to them. This method is used to enhance information embedded in speech signals such as device channel effects which could be instrumental in distinguishing genuine speech signals from replayed ones. Three features extracted using the adaptive filter process yielded performance improvements over other auditory concepts-based baselines, showing the potential of using an adaptive filter mechanism for replay spoofing attack detection.

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Wideband Speech and Audio Coding in the Perceptual Domain

Lin

Ambikairajah

Holmes

The International Series in Engineering and Computer Science

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Digital filter simulation of the basilar membrane

Cited by 17 publications

References 5 publications

An Adaptive-Q Cochlear Model for Replay Spoofing Detection

An Adaptive-Q Cochlear Model for Replay Spoofing Detection

Biologically Inspired Adaptive-Q Filterbanks for Replay Spoofing Attack Detection

Wideband Speech and Audio Coding in the Perceptual Domain

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