Noise reduction, which aims at estimating a clean speech from a noisy observation, has long been an active research area. The standard approach to this problem is to obtain the clean speech estimate by linearly filtering the noisy signal. The core issue, then, becomes how to design an optimal linear filter that can significantly suppress noise without introducing perceptually noticeable speech ...

In mobile devices, perceived speech signal degrades significantly in the presence of background noise as it reaches directly at the listener's ears. There is a need to improve the intelligibility and quality of the received speech signal in noisy environments by incorporating speech enhancement algorithms. This paper focuses on speech enhancement method including auditory masking propertie...

In mobile devices, perceived speech signal degrades significantly in the presence of background noise as it reaches directly at the listener's ears. There is a need to improve the intelligibility and quality of the received speech signal in noisy environments by incorporating speech enhancement algorithms. This paper focuses on speech enhancement method including auditory masking propertie...

In mobile devices, perceived speech signal degrades significantly in the presence of background noise as it reaches directly at the listener's ears. There is a need to improve the intelligibility and quality of the received speech signal in noisy environments by incorporating speech enhancement algorithms. This paper focuses on speech enhancement method including auditory masking propertie...

Speech enhancement is a long standing problem with various applications like hearing aids, automatic recognition and coding of speech signals. Single channel speech enhancement technique is used for enhancement of the speech degraded by additive background noises. The background noise can have an adverse impact on our ability to converse without hindrance or smoothly in very noisy environments,...

In this paper we propose an adaptive multi-modal verification system comprised of a modified Minimum Cost Bayesian Classifier (MCBC) and a method to find the reliability of the speech expert for various noisy conditions. The modified MCBC takes into account the reliability of each modality expert, allowing the de-emphasis of the contribution of opinions from the expert affected by noise. Reliab...

The separation of independent sources from mixed observed data is a fundamental and challenging signal processing problem. A method for directly extracting clean speech features from noisy speech is implemented. This process is based on independent component analysis (ICA) and a new feature analysis technique to reduce the computational complexity of the frequency-domain ICA. For noisy speech s...

This paper describes new method for the speaker adaptation of HMM parameters in environments with background noise. This method is based on Bayesian estimation, and calculates the a posteriori distribution of cleanspeech HMM parameters from their a priori distribution by using noisy speech observations. The advantage of the method is that the distribution of the noise can be taken into account ...

Missing data imputation estimates the clean speech features for automatic speech recognition in noisy environments. The estimates are usually considered equally reliable while in reality, the estimation accuracy varies from feature to feature. In this work, we propose uncertainty measures to characterise the expected accuracy of a sparse imputation (SI) based missing data method. In experiments...

Recently, lots of algorithms using machine learning approaches have been proposed in the speech enhancement area. One of the most well-known approaches is the non-negative matrix factorization (NMF) -based one which analyzes noisy speech with speech and noise bases. However, NMF-based algorithms have difficulties in estimating speech and noise encoding vectors when their subspaces overlap. In t...