Noise reduction algorithms in a generalized transform domain
IEEE Transactions on Audio, Speech, and Language Processing
On optimal frequency-domain multichannel linear filtering for noise reduction
IEEE Transactions on Audio, Speech, and Language Processing
New insights into the MVDR beamformer in room acoustics
IEEE Transactions on Audio, Speech, and Language Processing
Enabling user authentication and authorization to support context-aware UPnP applications
WebMedia '09 Proceedings of the XV Brazilian Symposium on Multimedia and the Web
Bait your hook: a novel detection technique for keyloggers
RAID'10 Proceedings of the 13th international conference on Recent advances in intrusion detection
On the ability of adaptation of speech signals and data hiding
Expert Systems with Applications: An International Journal
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Noise reduction, which aims at estimating a clean speech from noisy observations, has attracted a considerable amount of research and engineering attention over the past few decades. In the single-channel scenario, an estimate of the clean speech can be obtained by passing the noisy signal picked up by the microphone through a linear filter/transformation. The core issue, then, is how to find an optimal filter/transformation such that, after the filtering process, the signal-to-noise ratio (SNR) is improved but the desired speech signal is not noticeably distorted. Most of the existing optimal filters (such as the Wiener filter and subspace transformation) are formulated from the mean-square error (MSE) criterion. However, with the MSE formulation, many desired properties of the optimal noise-reduction filters such as the SNR behavior cannot be seen. In this paper, we present a new criterion based on the Pearson correlation coefficient (PCC). We show that in the context of noise reduction the squared PCC (SPCC) has many appealing properties and can be used as an optimization cost function to derive many optimal and suboptimal noise-reduction filters. The clear advantage of using the SPCC over the MSE is that the noise-reduction performance (in terms of the SNR improvement and speech distortion) of the resulting optimal filters can be easily analyzed. This shows that, as far as noise reduction is concerned, the SPCC-based cost function serves as a more natural criterion to optimize as compared to the MSE.