This paper presents the implementation of two nonlinear noise reduction methods applied to speech enhancement. The methods are based on embedding the noisy signal in a high-dimensional reconstructed phase space and applying singular value decomposition to project the signal into a lower dimension. The advantages of these nonlinear methods include that they do not require explicit models of nois...

In this paper, we evaluate a performance of in-car speech enhancement techniques with single channel signal processing and microphone array signal processing. We employ SS (Fourier Spectral Subtraction) and WSS (Wavelet Spectral Subtraction) as single channel signal processing and delay-and-sum beamformer, eigen beamformer, AMNOR (Adaptive Microphone array for NOise Reduction), S-AMNOR, Svc-AMN...

Spectral Subtraction based speech denoising suffers the major problem of introduction of new type of noise called musical noise. In this paper we introduce a two stage algorithm for denoising of noisy speech signal by minimizing the musical noise and additive white Gaussian noise. The algorithm works in two stages. First spectral subtraction method is applied on the noisy speech signal after ap...

In this paper, we propose a novel approach to robust speech recognition in noisy environments by discriminating the observation vectors. In conventional HMM-based speech recognition, all the observation vectors are treated with equal importance no matter how the corresponding speech segment is corrupted with noise. Our approach proposed here modifies the conventional decoder by weighting the li...

In speech recognition for real-world applications, the performance degradation due to the mismatch introduced between training and testing environments should be overcome. In this paper, to reduce this mismatch, we provide a hybrid method of spectral subtraction and residual noise masking. We also employ multiple model approach to obtain improved robustness over various noise environments. In t...

We show how to construct a channel-independent representation of speech that has propagated through a noisy reverberant channel. This is done by blindly rescaling the cepstral time series by a nonlinear function, with the form of this scale function being determined by previously encountered cepstra from that channel. The rescaled form of the time series is an invariant property of it in the fo...

A novel method to reduce additive non-stationary noise is proposed. The proposed method requires neither the statistical assumption about noise nor the estimate of the noise statistics from any pause regions. The enhancement is performed on a band-by-band basis for each time frame. Based on both the decision on whether a particular band in a frame is speech or noise dominant and the masking pro...

New robots for teleoperation and autonomous manipulation are increasingly being equipped with highbandwidth accelerometers for measuring the transient vibrational cues that occur during contact with objects. Unfortunately, the robot's own internal mechanisms often generate significant high-frequency accelerations, which we term ego-vibrations. This paper presents an approach to characterizing a...

Rapid visible/near-infrared (VNIR) hyperspectral imaging methods, employing both a single waveband algorithm and multi-spectral algorithms, were developed in order to discrimination between sound and discolored lettuce. Reflectance spectra for sound and discolored lettuce surfaces were extracted from hyperspectral reflectance images obtained in the 400-1000 nm wavelength range. The optimal wave...

This paper presents a spectral subtraction using the classifications between the speech dominant and the noise one. In our system, a new classification scheme between the speech dominant and the noise one is proposed. The proposed classifications use the standard deviation of the spectrum of observation signal in each critical band. We have introduced two oversubtraction factors for speech domi...