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Compressive speech enhancement makes use of the sparseness of speech and the non-sparseness of noise in time-frequency representation to perform speech enhancement. However, reconstructing the sparsest output may not necessarily translate to a good enhanced speech signal as speech distortion may be at risk. This paper proposes a two level optimization approach to incorporate objective quality measures in compressive speech enhancement. The proposed method combines the accelerated proximal gradient approach and a global one dimensional optimization method to solve the sparse reconstruction. By incorporating objective quality measures in the optimization process, the reconstructed output is not only sparse but also maintains the highest objective quality score possible. In other words, the sparse speech reconstruction process is now quality sparse speech reconstruction. Experimental results in a compressive speech enhancement consistently show score improvement in objectives measures in different noisy environments compared to the non-optimized method. Additionally, the proposed optimization yields a higher convergence rate with a lower computational complexity compared to the existing methods. [ABSTRACT FROM AUTHOR]

Blind signal separation has been studied extensively in order to tackle the cocktail party problem. It explores spatial diversity of the received mixtures of sources by different sensors. By using the kurtosis measure, it is possible to select the source of interest out of a number of separated BSS outputs. Further noise cancellation can be achieved by adding an adaptive noise canceller (ANC) as postprocessing. However, the computation is rather intensive and an online implementation of the overall system is not straightforward. This paper intends to fill the gap by developing an FPGA hardware architecture to implement the system. Subband processing is explored and detailed functional operations are profiled carefully. The final proposed FPGA system is able to handle signals with sample rate over 20000 samples per second. [ABSTRACT FROM AUTHOR]

The aim of this paper was to provide a derivation to explain the performance of the second-order-based blind signal separation (BSS) in reverberant environments. In particular, the second-order-based BSS algorithm, which exploits non-stationarity of the input signals, is investigated. The derivation provides a quantitative link between the reverberation parameter of the environment with the cost function of the BSS. Importantly, the theoretical finding complements existing literature on the interpretation of BSS and provides incremental insight to its performance. [ABSTRACT FROM AUTHOR]

Assistive hearing instruments have a significant impact on speech enhancement when the signal-to-noise ratio is low. These instruments are usually developed using the conventional adaptive gain equalizer (AGE), which has low computational complexity and low distortion in real-time speech enhancement. The conventional AGEs are intended to boost the speech segments of speech signals but they are incapable of suppressing noise segments. The overall speech quality of the assistive hearing instruments may be reduced, as the noise segments still cannot be filtered out. In this paper, a decision-directed AGE is proposed for assistive hearing instruments. It aims to overcome the limitation of the conventional AGE, which is capable only of boosting speech segments in noisy speech but incapable of suppressing noise segments. The proposed approach simultaneously boosts the speech segments and suppresses noise segments in noisy speech. Experimental results with different types of real-world noise indicate that the proposed method achieves better speech quality than does the conventional AGE. The resulting method provides an improved functionality for assistive hearing instruments. [ABSTRACT FROM PUBLISHER]

Highlights: [•] We study the signal enhancement problem. [•] We propose a multi-filter system. [•] We study the multi-criteria design of the system. [•] We propose an implementation of the system with an FPGA embedded system. [ABSTRACT FROM AUTHOR]