The performance of speaker recognition systems is very well on the datasets without noise and mismatch. However, the performance gets degraded with the environmental noises, channel variation, physical and behavioral changes in speaker. The types of Speaker related feature play crucial role in improving the performance of speaker recognition systems. Gammatone Frequency Cepstral Coefficient (GFCC) features has been widely used to develop robust speaker recognition systems with the conventional machine learning, it achieved better performance compared to Mel Frequency Cepstral Coefficient (MFCC) features in the noisy condition. Recently, deep learning models showed better performance in the speaker recognition compared to conventional machine learning. Most of the previous deep learning-based speaker recognition models has used Mel Spectrogram and similar inputs rather than a handcrafted features like MFCC and GFCC features. However, the performance of the Mel Spectrogram features gets degraded in the high noise ratio and mismatch in the utterances. Similar to Mel Spectrogram, Cochleogram is another important feature for deep learning speaker recognition models. Like GFCC features, Cochleogram represents utterances in Equal Rectangular Band (ERB) scale which is important in noisy condition. However, none of the studies have conducted analysis for noise robustness of Cochleogram and Mel Spectrogram in speaker recognition. In addition, only limited studies have used Cochleogram to develop speech-based models in noisy and mismatch condition using deep learning. In this study, analysis of noise robustness of Cochleogram and Mel Spectrogram features in speaker recognition using deep learning model is conducted at the Signal to Noise Ratio (SNR) level from −5 dB to 20 dB. Experiments are conducted on the VoxCeleb1 and Noise added VoxCeleb1 dataset by using basic 2DCNN, ResNet-50, VGG-16, ECAPA-TDNN and TitaNet Models architectures. The Speaker identification and verification performance of both Cochleogram and Mel Spectrogram is evaluated. The results show that Cochleogram have better performance than Mel Spectrogram in both speaker identification and verification at the noisy and mismatch condition.

Reconstructing high quality and consistent Magnetic Resonance images (MRI) from low under sampled k- space data were a common challenges in medical image construction and analysis. To overcome these challenges, it needs an advanced reconstruction technique. Currently, various deep learning techniques are outperforming traditional medical image reconstruction techniques, like parallel imaging and compressed Sensing techniques. However these techniques performed well in high under sampled data, they will produce low performance when dealing with low under sampled k-space data. In this work, we proposed a generative adversarial network (GAN) based architecture called HARA-GAN by integrating residual U-net with Hybrid attention and Relative average discriminator, to modify MRI reconstruction and reduce the noise caused by low under sampling rates. The zero filling image was generated using 12.5% under sampling rates for cartesian, and 10% under sampling rates for both radial and spiral under sampling masks. In generator module the proposed method used two residual U-net with hybrid attention mechanism. In encoder parts of the U-net, we employed an spatial and self attention and in the decoder side we have employed spatial and channel wise attention techniques. The reconstructed MR image quality and consistency was evaluated using peak signal-to-noise ratio (PSNR), structural similarity index measures (SSIM), and normalized mean square error (NMSE). The results indicated that HARA-GAN outperforms DAGAN, RefineGAN and RSCA-GAN methods based on error maps and quantitative evaluation metrics in terms of both image quality and consistencies on two prominent data sets, brain and knee MR image reconstruction. Over all, HARA-GAN have important implications for clinical applications, as high-quality and consistent results are crucial for accurate diagnosis and treatments of brain and knee MR images.