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Computational auditory models in predicting noise reduction performance for wideband telephony applications

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Abstract

The performance of several noise reduction algorithms intended for wideband telephony was evaluated both subjectively and objectively. The chosen algorithms were based on statistical modeling, spectral subtraction, Wiener filtering, or subspace modelling principles. A customized wideband noise reduction database containing speech samples corrupted by three types of background noises at three SNR levels, along with their enhanced versions was created. The overall quality of the speech samples in the database was subsequently rated by a group of listeners with normal hearing capabilities. Comprehensive statistical analyses were performed to assess the reliability of the subjective data, and to assess the performance of noise reduction algorithms across varied noisy conditions. The subjective quality ratings were then used to investigate the performance of several auditory model-based objective quality metrics. Key results from these investigations include: (a) there was a high degree of inter- and intra-subject reliability in the subjective ratings, (b) noise reduction algorithms enhance speech quality for only a subset of the noise conditions, and (c) auditory model-based metrics perform similarly in predicting speech quality ratings, when speech quality scores pertaining to a particular noise condition were averaged.

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Notes

  1. LogMMSE and LogMMSE_SPU are from the same class of noise reduction algorithms and their implementations are also same with the difference that LogMMSE_SPU algorithm is based on the fact that speech may not be present at all time and there are some pause periods even during speech activity. This Speech Presence Uncertainty (SPU) is taken into account by involving a factor which shows the probability of the presence of the speech at a particular frequency (Loizou 2007).

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Acknowledgements

This work was supported by Ontario Research Fund and Research In Motion Company. The authors would like to thank Chris Forrester, Malay Gupta and Nikolai Kouznetsov for their helpful and supportive comments.

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  1. National Centre for Audiology & Dept. of Electrical and Computer Engineering, University of Western Ontario, London, ON, N6G 1H1, Canada

    Nazanin Pourmand, Vijay Parsa & Angela Weaver

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  1. Nazanin Pourmand
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  2. Vijay Parsa
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  3. Angela Weaver
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Correspondence to Nazanin Pourmand.

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Pourmand, N., Parsa, V. & Weaver, A. Computational auditory models in predicting noise reduction performance for wideband telephony applications. Int J Speech Technol 16, 363–379 (2013). https://doi.org/10.1007/s10772-013-9189-1

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