Single Channel Blind Dereverberation of Speech Signals

Dereverberation of recorded speech signals is one of the most pertinent problems in speech processing. In the present work, the objective is to understand and implement dereverberation techniques that aim at enhancing the magnitude spectrogram of reverberant speech signals to remove the reverberant effects introduced. An approach to estimate a clean speech spectrogram from the reverberant speech spectrogram is proposed. This is achieved through non-negative matrix factor deconvolution(NMFD). Further, this approach is extended using the NMF representation for speech magnitude spectrograms. To exploit temporal dependencies, a convolutive NMF-based representation and a frame-stacked model are incorporated into the NMFD framework for speech. A novel approach for dereverberation by applying NMFD to the activation matrix of the reverberated magnitude spectrogram is also proposed. Finally, a comparative analysis of the performance of the listed techniques, using sentence recordings from the TIMIT database and recorded room impulse responses from the Reverb 2014 challenge, is presented based on two key objective measures - PESQ and Cepstral Distortion.\\ Although we were qualitatively able to verify the claims made in literature regarding these techniques, exact results could not be matched. The novel approach, as it is suggested, provides improvement in quantitative metrics, but is not consistent

ForeCal: Random Forest-based Calibration for DNNs

Deep neural network(DNN) based classifiers do extremely well in discriminating between observations, resulting in higher ROC AUC and accuracy metrics, but their outputs are often miscalibrated with respect to true event likelihoods. Post-hoc calibration algorithms are often used to calibrate the outputs of these classifiers. Methods like Isotonic regression, Platt scaling, and Temperature scaling have been shown to be effective in some cases but are limited by their parametric assumptions and/or their inability to capture complex non-linear relationships. We propose ForeCal - a novel post-hoc calibration algorithm based on Random forests. ForeCal exploits two unique properties of Random forests: the ability to enforce weak monotonicity and range-preservation. It is more powerful in achieving calibration than current state-of-the-art methods, is non-parametric, and can incorporate exogenous information as features to learn a better calibration function. Through experiments on 43 diverse datasets from the UCI ML repository, we show that ForeCal outperforms existing methods in terms of Expected Calibration Error(ECE) with minimal impact on the discriminative power of the base DNN as measured by AUC.