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Under real life condition, speech signal is often, corrupted with several noise types. To attenuate this issue, a noise reduction phase is performed before analyzing emotional speech using enhancement algorithms. Three speech enhancement algorithms are introduced for improved emotion classification; spectral subtraction, wiener filter and MMSE. Experiments were prepared with MFCC as feature vectors...
The paper presents a new speech enhancement algorithm using feedback particle filter. The innovation error based feedback structure is the decisive factor for Feedback particle filter. The robustness is key feature, this variant is found to improve the performance of the system reducing MSE as compared to conventional particle filter at even lower no of particles, without much preprocessing or modeling,...
We introduce a model of communication that includes noise inherent in the message production process as well as noise inherent in the message interpretation process. The production and interpretation noise processes have a fixed signal-to-noise ratio. The resulting system is a simple but effective model of human communication. The model naturally leads to a method to enhance the intelligibility of...
We introduce a method for the concealment of a missing segment in an audio signal. The method is based on sinusoidal interpolation and extrapolation operations. An extrapolation is always followed by an interpolation and an interpolation is always followed by observed data. For interpolation, sinusoids are detected in both the observations prior and after the missing data segments. All sinusoids together...
Advances in hardware and communication technology make distributed sound acquisition increasingly attractive. We describe a distributed beamforming method based on the diffusion adaptation paradigm. In contrast to existing distributed beamforming methods, the method does not impose conditions on the topology or the structure of the network nor does it require knowledge of the noise co-variance matrix...
In an audio speech signal, acoustic noise is a common problem while the speech is processed. Here, we are going to create color noise and add with an audio signal, after that a model are introduced to eliminate that noise. This paper elaborates a new approach for noise cancellation in speech enhancement using an Adaptive LMS (Least Mean Square) filter and with the help of MATLAB Simulink we get the...
In this paper, a new and quick method is introduced for speech enhancement. The base or basic of this method is due to filtering the singular value which is obtained from SVD. The efficiency of the proposed methods is its strong capability and the speed of in reduction the noise effect and also does not have the typical “musical tone”, which is usually present in other noise reduction methods. The...
Speech transients have been shown to be important cues for identifying and discriminating speech sounds. We previously described a wavelet packet-based method for extracting transient speech (Rasetshwane et al. WASPAA 2007, pp. 179-182). The algorithm uses a ldquotransitivity functionrdquo to characterize the rate of change of wavelet coefficients, and it can be implemented in real-time to process...
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