Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Speech Communication - Special issue on speech processing in adverse conditions
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
The Modulation Spectrogram: In Pursuit of an Invariant Representation of Speech
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Multiple statistical models for soft decision in noisy speech enhancement
Pattern Recognition
A Laplacian-based MMSE estimator for speech enhancement
Speech Communication
Pattern Recognition Letters
Subjective comparison and evaluation of speech enhancement algorithms
Speech Communication
Finding Pitch Markers using First Order Gaussian Differentiator
ICISIP '05 Proceedings of the 2005 3rd International Conference on Intelligent Sensing and Information Processing
Reverberant speech enhancement by temporal and spectral processing
IEEE Transactions on Audio, Speech, and Language Processing
Computing the discrete-time “analytic” signal via FFT
IEEE Transactions on Signal Processing
Event-Based Instantaneous Fundamental Frequency Estimation From Speech Signals
IEEE Transactions on Audio, Speech, and Language Processing
Evaluation of Objective Quality Measures for Speech Enhancement
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
De-noising by soft-thresholding
IEEE Transactions on Information Theory
International Journal of Speech Technology
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This paper presents a noisy speech enhancement method by combining linear prediction (LP) residual weighting in the time domain and spectral processing in the frequency domain to provide better noise suppression as well as better enhancement in the speech regions. The noisy speech is initially processed by the excitation source (LP residual) based temporal processing that involves identifying and enhancing the excitation source based speech-specific features present at the gross and fine temporal levels. The gross level features are identified by estimating the following speech parameters: sum of the peaks in the discrete Fourier transform (DFT) spectrum, smoothed Hilbert envelope of the LP residual and modulation spectrum values, all from the noisy speech signal. The fine level features are identified using the knowledge of the instants of significant excitation. A weight function is derived from the gross and fine weight functions to obtain the temporally processed speech signal. The temporally processed speech is further subjected to spectral domain processing. Spectral processing involves estimation and removal of degrading components, and also identification and enhancement of speech-specific spectral components. The proposed method is evaluated using different objective and subjective quality measures. The quality measures show that the proposed combined temporal and spectral processing method provides better enhancement, compared to either temporal or spectral processing alone.