Modeling auditory processing of amplitude modulation. I. Detection and masking with narrow-band carriers

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Abstract

This paper presents a quantitative model for describing data from modulation-detection and modulation-masking experiments, which extends the model of the "effective" signal processing of the auditory system described in Dau et al. [J. Acoust. Soc. Am. 99, 3615-3622 (1996)]. The new element in the present model is a modulation filterbank, which exhibits two domains with different scaling. In the range 0-10 Hz, the modulation filters have a constant bandwidth of 5 Hz. Between 10 Hz and 1000 Hz a logarithmic scaling with a constant Q value of 2 was assumed. To preclude spectral effects in temporal processing, measurements and corresponding simulations were performed with stochastic narrow-band noise carriers at a high center frequency (5 kHz). For conditions in which the modulation rate (fmod) was smaller than half the bandwidth of the carrier (delta f), the model accounts for the low-pass characteristic in the threshold functions [e.g., Viemeister, J. Acoust. Soc. Am. 66, 1364-1380 (1979)]. In conditions with fmod > delta f/2, the model can account for the high-pass characteristic in the threshold function. In a further experiment, a classical masking paradigm for investigating frequency selectivity was adopted and translated to the modulation-frequency domain. Masked thresholds for sinusoidal test modulation in the presence of a competing modulation masker were measured and simulated as a function of the test modulation rate. In all cases, the model describes the experimental data to within a few dB. It is proposed that the typical low-pass characteristic of the temporal modulation transfer function observed with wide-band noise carriers is not due to "sluggishness" in the auditory system, but can instead be understood in terms of the interaction between modulation filters and the inherent fluctuations in the carrier.

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A quantitative model of the "effective" signal processing in the auditory system. I. Model structure.

Torsten Dau, Dirk Püschel, Armin Kohlrausch (1996)

This paper describes a quantitative model for signal processing in the auditory system. The model combines a series of preprocessing stages with an optimal detector as the decision device. The present paper gives a description of the various preprocessing stages and of the implementation of the optimal detector. The output of the preprocessing stages is a time-varying activity pattern to which "internal noise" is added. In the decision process, a stored temporal representation of the signal to be detected (template) is compared with the actual activity pattern. The comparison amounts to calculating the correlation between the two temporal patterns and is comparable to a "matched filtering" process. The detector itself derives the template at the beginning of each simulated threshold measurement from a suprathreshold value of the stimulus. The model allows one to estimate thresholds with the same signals and psychophysical procedures as those used in actual experiments. In the accompanying paper [Dau et al., J. Acoust. Soc. Am. 99, 3623-3631 (1996)] data obtained for human observers are compared with the optimal-detector model for various masking conditions.

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Periodicity coding in the inferior colliculus of the cat. I. Neuronal mechanisms.

Rebecca G Langner, C E Schreiner (1988)

1. Temporal properties of single- and multiple-unit responses were investigated in the inferior colliculus (IC) of the barbiturate-anesthetized cat. Approximately 95% of recording sites were located in the central nucleus of the inferior colliculus (ICC). Responses to contralateral stimulation with tone bursts and amplitude-modulated tones (100% sinusoidal modulation) were recorded. Five response parameters were determined for neurons at each location: 1) characteristic frequency (CF); 2) onset latency of responses to CF-tones 60 dB above threshold; 3) Q10 dB (CF divided by bandwidth of tuning curve 10 dB above threshold); 4) best modulation frequency for firing rate (rBMF or BMF; amplitude modulation frequency that elicited the highest firing rate); and 5) best modulation frequency for synchronization (sBMF; amplitude modulation frequency that elicited the highest degree of phase-locking to the modulation frequency). 2. Response characteristics for single units and multiple units corresponded closely. A BMF was obtained at almost all recording sites. For units with a similar CF, a range of BMFs was observed. The upper limit of BMF increased approximately proportional to CF/4 up to BMFs as high as 1 kHz. The lower limit of encountered BMFs for a given CF also increased slightly with CF. BMF ranges for single-unit and multiple-unit responses were similar. Twenty-three percent of the responses revealed rBMFs between 10 and 30 Hz, 51% between 30 and 100 Hz, 18% between 100 and 300 Hz, and 8% between 300 and 1000 Hz. 3. For single units with modulation transfer functions of bandpass characteristics, BMFs determined for firing rate and synchronization were similar (r2 = 0.95). 4. Onset latencies for responses to CF tones 60 dB above threshold varied between 4 and 120 ms. Ninety percent of the onset latencies were between 5 and 18 ms. A range of onset latencies was recorded for different neurons with any given CF. The onset response latency of a given unit or unit cluster was significantly correlated with the period of the BMF and the period of the CF (P less than 0.05). 5."Intrinsic oscillations" of short duration, i.e., regularly timed discharges of units in response to stimuli without a corresponding temporal structure, were frequently observed in the ICC. Oscillation intervals were commonly found to be integer multiples of 0.4 ms. Changes of stimulus frequency or intensity had only minor influences on these intrinsic oscillations.(ABSTRACT TRUNCATED AT 400 WORDS)

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Modeling auditory processing of amplitude modulation. II. Spectral and temporal integration.

A Kohlrausch, B Kollmeier, T Dau (1997)

A multi-channel model, describing the effects of spectral and temporal integration in amplitude-modulation detection for a stochastic noise carrier, is proposed and validated. The model is based on the modulation filterbank concept which was established in the accompanying paper [Dau et al., J. Acoust. Soc. Am. 102, 2892-2905 (1997)] for modulation perception in narrow-band conditions (single-channel model). To integrate information across frequency, the detection process of the model linearly combines the channel outputs. To integrate information across time, a kind of "multiple-look" strategy, is realized within the detection stage of the model. Both data from the literature and new data are used to validate the model. The model predictions agree with the results of Eddins [J. Acoust. Soc. Am. 93, 470-479 (1993)] that the "time constants" associated with the temporal modulation transfer functions (TMTF) derived for narrow-band stimuli do not vary with carrier frequency region and that they decrease monotonically with increasing stimulus bandwidth. The model is able to predict masking patterns in the modulation-frequency domain, as observed experimentally by Houtgast [J. Acoust. Soc. Am. 85, 1676-1680 (1989)]. The model also accounts for the finding by Sheft and Yost [J. Acoust. Soc. Am. 88, 796-805 (1990)] that the long "effective" integration time constants derived from the data are two orders of magnitude larger than the time constants derived from the cutoff frequency of the TMTF. Finally, the temporal-summation properties of the model allow the prediction of data in a specific temporal paradigm used earlier by Viemeister and Wakefield [J. Acoust. Soc. Am. 90, 858-865 (1991)]. The combination of the modulation filterbank concept and the optimal decision algorithm proposed here appears to present a powerful strategy for describing modulation-detection phenomena in narrow-band and broadband conditions.