Applies to version: 0.10.0

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JELFS2011 - Predicted binaural advantage for speech in reverberant conditions


[benefit weighted_SNR weighted_bmld] = jelfs2011(target,interferer,fs)

Input parameters

target Binaural target impulse respone (or stimulus)
interfererer Binaural interferer impulse response (or stimulus) Multiple interfering impulse responses MUST be concatenated, not added.

Output parameters

benefit spatial release from masking (SRM)in dB
weighted_SNR component of SRM due to better-ear listening (dB)
weighted_bmld component of SRM due to binaural unmasking (dB)

jelfs2011(target,interferer,fs) computes the increase in speech intelligibility of the target when the target and interferer are spatially separated. They are preferably represented by their impulse responses, but can be represented by noise recordings of equivalent spectral shape emitted from the same source locations (using the same noise duration for target and interferer). The impulse responses are assumed to be sampled at a sampling frequency of fs Hz. If the modelled sources differ in spectral shape, this can be simulated by pre-filtering the impulse responses.

[benefit, weighted_SNR, weighted_bmld]=jelfs2011(...) additionaly returns the benefit from the SII weighted SNR and the SII weighted BMLD.

If target or interferer are cell-arrays, the HRTF data will be loaded. The first argument in the cell-array is the azimuth angle, and the second parameter is the database type. The elevation is set to zero. function.


The following code will load HRIRs from the 'kemar' database and compute the binaural speech intelligibility advantage for a target at 0 degrees and interferers at 300 and 90 degrees:

jelfs2011({0,'kemar'},{[330 90],'kemar'})

This code produces the following output:

ans =



J. Culling, S. Jelfs, and M. Lavandier. Mapping Speech Intelligibility in Noisy Rooms. In Proceedings of the 128th convention of the Audio Engineering Society, Convention paper 8050, 2010.

S. Jelfs, J. Culling, and M. Lavandier. Revision and validation of a binaural model for speech intelligibility in noise. Hearing Research, 2011.

M. Lavandier, S. Jelfs, J. Culling, A. Watkins, A. Raimond, and S. Makin. Binaural prediction of speech intelligibility in reverberant rooms with multiple noise sources. J. Acoust. Soc. Am., 131(1):218--231, 2012.