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Found 12 papers, 4 papers with code
Domain-Generalized Face Anti-Spoofing with Unknown Attacks
Although face anti-spoofing (FAS) methods have achieved remarkable performance on specific domains or attack types, few studies have focused on the simultaneous presence of domain changes and unknown attacks, which is closer to real application scenarios.
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.
Linear Classifier: An Often-Forgotten Baseline for Text Classification
Large-scale pre-trained language models such as BERT are popular solutions for text classification.
Adapting pretrained speech model for Mandarin lyrics transcription and alignment
With the use of data augmentation and source separation model, results show that the proposed method achieves a character error rate of less than 18% on a Mandarin polyphonic dataset for lyrics transcription, and a mean absolute error of 0. 071 seconds for lyrics alignment.
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.
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.
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.
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.
NASTAR: Noise Adaptive Speech Enhancement with Target-Conditional Resampling
NASTAR uses a feedback mechanism to simulate adaptive training data via a noise extractor and a retrieval model.
Novel Preprocessing Technique for Data Embedding in Engineering Code Generation Using Large Language Model
We present four main contributions to enhance the performance of Large Language Models (LLMs) in generating domain-specific code: (i) utilizing LLM-based data splitting and data renovation techniques to improve the semantic representation of embeddings' space; (ii) introducing the Chain of Density for Renovation Credibility (CoDRC), driven by LLMs, and the Adaptive Text Renovation (ATR) algorithm for assessing data renovation reliability; (iii) developing the Implicit Knowledge Expansion and Contemplation (IKEC) Prompt technique; and (iv) effectively refactoring existing scripts to generate new and high-quality scripts with LLMs.
Improving Facial Landmark Detection Accuracy and Efficiency with Knowledge Distillation
The domain of computer vision has experienced significant advancements in facial-landmark detection, becoming increasingly essential across various applications such as augmented reality, facial recognition, and emotion analysis.
