Making meaningful comparisons between the performance of the various speech enhancement algorithms proposed over the years, has been elusive due to lack of a common speech database, differences in the types of noise used and differences in the testing methodology. To facilitate such comparisons, we report on the development of a noisy speech corpus suitable for evaluation of speech enhancement ...

The majority of current speech enhancement systems are based on generalized signal-to-noise ratio dependent weighting rules and do not take into account the characteristics of the actual speech sound being processed. The following contribution is concerned with phoneme-specific speech enhancement methods that apply specially tailored signal processing methods. The first signal processing algori...

This paper presents a speech processing platform which can be used to demonstrate and investigate speech enhancement methods. This platform is called Speech Enhancement for Android (SEA), and has been developed on the Android operating system, available to students, teaching staff and researchers through the Android market. SEA can be used as an additional teaching tool in undergraduate courses...

This paper investigates the effect of Gaussian window frequency response Side lobe Attenuation on the improvement of Speech quality in terms of six objective quality measures. In Speech Enhancement process, signal corrupted by noise is segmented into frames and each segment is Windowed using Gaussian window with variation in the side lobe attenuation parameter “α”. The Windowed Speech segments ...

Convolutional neural network (CNN) based methods, such as the convolutional encoder–decoder network, offer state-of-the-art results in monaural speech enhancement. In conventional large kernel size is often used to enhance model capacity, which, however, low parameter efficiency. This could be addressed by using group convolution, AlexNet, where convolutions are performed parallel each layer, b...