# THE AUDITORY MODELING TOOLBOX

Applies to version: 0.10.0

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# joergensen2013 - the multi-resolution sEPSM

## Usage

output = joergensen2013(x, y, fs, IO_param)


output = joergensen2013(x, y, fs, IO_param) calculates the signal-to-noise envelope-power (SNRenv) ratio using the multi-resolution speech-based envelope spectrum model (mr-sEPSM) described in Joergensen et al. (2013).

## Input parameters

 '',x noisy speech mixture '',y noise alone '',fs sample rate in Hz '',IO_param (optional) vector with parameters for the ideal observer that converts the SNRenv to probability of correct, assuming a given speech material. It contains four parameters of the ideal observer formatted as [k q m sigma_s].

## Output parameters

'=output.SNRenv'
The SNRenv
'=output.P_correct'
The probability of correct given the SNRenv. This field is only included if IO_param is specified. Its calculation requires the Statistics ToolBox.

The model is based on the model from Joergensen et al. (2011), which consists of the following stages:

1. A gammatone bandpass filterbank to simulate the auditory filters
2. An envelope extraction stage via the Hilbert Transform
3. A modulation filterbank
4. Computation of the long-term envelope power (output.SNRenv)

5) A decision mechanism based on a statistically ideal observer (output.P_correct)

The main difference between to the Joergensen et al. (2011) model is that the present model estimates the envelope power using multi-resolution segmentation of the envelope. The segment duration depends on the modulation filter center-frequency. In addition, the modulation filter bank includes filters up to modulation frequencies of 256 Hz in contrast to the 64 Hz considered by the model from Joergensen et al. (2011).

## References:

S. Joergensen and T. Dau. Predicting speech intelligibility based on the signal-to-noise envelope power ratio after modulation-frequency selective processing. J. Acoust. Soc. Am., 130(3):1475--1487, 2011.

S. Jørgensen, S. D. Ewert, and T. Dau. A multi-resolution envelope power based model for speech intelligibility. J. Acoust. Soc. Am., 134(1):436--446, 2013.