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Deep neural network is now a new trend towards solving different problems in speech processing. In this paper, we propose a discriminative deep recurrent neural network (DRNN) model for monaural speech separation. Our idea is to construct DRNN as a regression model to discover the deep structure and regularity for signal reconstruction from a mixture of two source spectra. To reinforce the discrimination...
This paper presents a single-channel high-dimensionalWiener filter in the spectro-temporal modulation domain. Unlike other conventional noise reduction techniques, the proposed algorithm not only reduces noise but also enhances the “textures” of the speech signal. A non-iterative decision-directed noise estimation method is adopted to estimate the modulation SNR for the modulation-domain Wiener filter...
A hearing model, which is parameterized by hearing thresholds, degrees of loudness recruitment and reductions of frequency resolution of a hearing-impaired (HI) patient, is proposed in this paper. The model is developed in the filter-bank framework and is flexible for fitting hearing-loss conditions of HI patients. Psychoacoustic experiments were conducted under clean and noisy conditions to validate...
This paper proposes a non-uniformly distributed threemicrophone array speech enhancement system to suppress directional interferences and diffuse noise simultaneously. Each pair of microphones is designed to tackle one kind of noise. Unlike other hybrid systems, which combine different noise suppression techniques derived in different domains, the proposed system integrates two noise suppression techniques...
In this paper, we propose a voice activity detection (VAD) algorithm based on spectro-temporal modulation structures of input sounds. A multi-resolution spectro-temporal analysis framework is used to inspect prominent speech structures. By comparing with an adaptive threshold, the proposed VAD distinguishes speech from non-speech based on the energy of the frequency modulation of harmonics. Compared...
In this paper, we propose a signal-channel speech enhancement algorithm by applying the conventional Wiener filter in the spectro-temporal modulation domain. The multi-resolution spectro-temporal analysis and synthesis framework for Fourier spectrograms [12] is extended to the analysis-modification-synthesis (AMS) framework for speech enhancement. Compared with conventional speech enhancement algorithms,...
The concept of the two-dimensional spectro-temporal modulation filtering of the auditory model [1] is implemented for the FFT spectrogram. It analyzes the spectrogram in terms of the temporal dynamics and the spectral structures of the sound. The overlap and add (OLA) method, which is more convenient and reliable than the iterative-projection method proposed in [1], is used to invert the FFT spectrogram...
The performance of conventional speaker identification systems is severely compromised by interference, such as additive or convolutional noises. High-level information of the speaker provides more robust cues for identifying speakers. This paper proposes an auditory-model based spectro-temporal modulation filtering (STMF) process to capture high-level information for robust speaker identification...
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