Instruct: Given a paper title, retrieve paper titles that are cited by the given paper
Query: SNR-Aware Convolutional Neural Network Modeling for Speech Enhancement.
An Experimental Study on Speech Enhancement Based on Deep Neural Networks
A speech enhancement algorithm by iterating single- and multi-microphone processing and its application to robust ASR
Speech enhancement with missing data techniques using recurrent neural networks
Speech enhancement in nonstationary noise environments using noise properties
A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancement
Enhancement and bandwidth compression of noisy speech
Radio Model-free Noise Reduction of Radio Transmissions with Convolutional Autoencoders
Noise Adaptive Speech Enhancement using Domain Adversarial Training
Convolutional Neural Networks-based continuous speech recognition using raw speech signal
Recurrent convolutional neural network for speech processing
TROPE R HCRAESE R PAID I END-TO-END ACOUSTIC MODELING USING CONVOLUTIONAL NEURAL NETWORKS FOR AUTOMATIC SPEECH RECOGNITION
Gated Residual Networks with Dilated Convolutions for Supervised Speech Separation
Lip-Reading Driven Deep Learning Approach for Speech Enhancement
Unseen noise estimation using separable deep auto encoder for speech enhancement
A transfer learning and progressive stacking approach to reducing deep model sizes with an application to speech enhancement
Speech enhancement based on wavelet thresholding the multitaper spectrum
Adversarial training for data-driven speech enhancement without parallel corpus
Robust speech recognition with speech enhanced deep neural networks.
Fully Convolutional Speech Recognition
Convolutional Neural Networks for Speech Recognition
Mechanisms of Disease: pathogenesis of Crohn's disease and ulcerative colitis
Multi-arm snake-like robot
Moodbile: A Framework to Integrate m-Learning Applications with the LMS
Reproductive biology of Venus clam Gafrarium tumidum (Roding, 1798) from Southeast coast of India
Suppression of acoustic noise in speech using spectral subtraction