AN EVALUATION OF NOISE POWER SPECTRAL DENSITY ESTIMATION ALGORITHMS IN ADVERSE ACOUSTIC ENVIRONMENTS

Speech Enhancement

Přednášející: Jalal Taghia, Autoři: Jalal Taghia, Ruhr-Universität Bochum, Germany; Jalil Taghia, Nasser Mohammadiha, KTH - Royal Institute of Technology, Sweden; Jinqiu Sang, University of Southampton, United Kingdom; Vaclav Bouse, Siemens Audiological Engineering Group, Germany; Rainer Martin, Ruhr-Universität Bochum, Germany

Noise power spectral density estimation is an important component of speech enhancement systems due to its considerable effect on the quality and the intelligibility of the enhanced speech. Recently, many new algorithms have been proposed and significant progress in noise tracking has been made. In this paper, we present an evaluation framework for measuring the performance of some recently proposed and some well-known noise power spectral density estimators and compare their performance in adverse acoustic environments. In this investigation we do not only consider the performance in the mean of a spectral distance measure but also evaluate the variance of the estimators as the latter is related to undesirable fluctuations also known as musical noise. By providing a variety of different non-stationary noises, the robustness of noise estimators in adverse environments is examined.

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