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Found 6 papers, 1 papers with code
SEOFP-NET: Compression and Acceleration of Deep Neural Networks for Speech Enhancement Using Sign-Exponent-Only Floating-Points
In this paper, a novel sign-exponent-only floating-point network (SEOFP-NET) technique is proposed to compress the model size and accelerate the inference time for speech enhancement, a regression task of speech signal processing.
Speech Recovery for Real-World Self-powered Intermittent Devices
The incompleteness of speech inputs severely degrades the performance of all the related speech signal processing applications.
SERIL: Noise Adaptive Speech Enhancement using Regularization-based Incremental Learning
The results verify that the SERIL model can effectively adjust itself to new noise environments while overcoming the catastrophic forgetting issue.
Stock Prices Prediction using Deep Learning Models
The results show that predicting stock price through price rate of change is better than predicting absolute prices directly.
A study on speech enhancement using exponent-only floating point quantized neural network (EOFP-QNN)
We evaluated the proposed EOFP quantization technique on two types of neural networks, namely, bidirectional long short-term memory (BLSTM) and fully convolutional neural network (FCN), on a speech enhancement task.
