6 research outputs found
Deep Neural Networks for Speech Enhancement in Complex-Noisy Environments
In this paper, we considered the problem of the speech enhancement similar to the real-world environments where several complex noise sources simultaneously degrade the quality and intelligibility of a target speech. The existing literature on the speech enhancement principally focuses on the presence of one noise source in mixture signals. However, in real-world situations, we generally face and attempt to improve the quality and intelligibility of speech where various complex stationary and nonstationary noise sources are simultaneously mixed with the target speech. Here, we have used deep learning for speech enhancement in complex-noisy environments and used ideal binary mask (IBM) as a binary classification function by using deep neural networks (DNNs). IBM is used as a target function during training and the trained DNNs are used to estimate IBM during enhancement stage. The estimated target function is then applied to the complex-noisy mixtures to obtain the target speech. The mean square error (MSE) is used as an objective cost function at various epochs. The experimental results at different input signal-to-noise ratio (SNR) showed that DNN-based complex-noisy speech enhancement outperformed the competing methods in terms of speech quality by using perceptual evaluation of speech quality (PESQ), segmental signal-to-noise ratio (SNRSeg), log-likelihood ratio (LLR), weighted spectral slope (WSS). Moreover, short-time objective intelligibility (STOI) reinforced the better speech intelligibility
Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications
Multimodality Representation Learning, as a technique of learning to embed
information from different modalities and their correlations, has achieved
remarkable success on a variety of applications, such as Visual Question
Answering (VQA), Natural Language for Visual Reasoning (NLVR), and Vision
Language Retrieval (VLR). Among these applications, cross-modal interaction and
complementary information from different modalities are crucial for advanced
models to perform any multimodal task, e.g., understand, recognize, retrieve,
or generate optimally. Researchers have proposed diverse methods to address
these tasks. The different variants of transformer-based architectures
performed extraordinarily on multiple modalities. This survey presents the
comprehensive literature on the evolution and enhancement of deep learning
multimodal architectures to deal with textual, visual and audio features for
diverse cross-modal and modern multimodal tasks. This study summarizes the (i)
recent task-specific deep learning methodologies, (ii) the pretraining types
and multimodal pretraining objectives, (iii) from state-of-the-art pretrained
multimodal approaches to unifying architectures, and (iv) multimodal task
categories and possible future improvements that can be devised for better
multimodal learning. Moreover, we prepare a dataset section for new researchers
that covers most of the benchmarks for pretraining and finetuning. Finally,
major challenges, gaps, and potential research topics are explored. A
constantly-updated paperlist related to our survey is maintained at
https://github.com/marslanm/multimodality-representation-learning