Speech enhancement is a critical task in various applications, including hearing aid devices, voice assistants, and communication systems. Deep learning (DL) has revolutionized this field by offering advanced techniques to improve speech quality. This survey provides an overview of the current state-of-the-art DL approaches for speech enhancement, highlighting their strengths, weaknesses, and future research directions.
Section 1: Background and Related Work
Speech enhancement is a complex task that involves mitigating the interference caused by noise and reverberation in audio signals. Traditional methods rely on handcrafted features and linear models, which have limitations in terms of their ability to handle complex noise scenarios. The advent of DL has enabled the development of more sophisticated models that can learn to extract relevant features from raw audio data and perform enhancement tasks with high accuracy.
Section 2: Deep Learning Architectures for Speech Enhancement
Several DL architectures have been proposed for speech enhancement, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models. These architectures are designed to capture the temporal and spectral characteristics of speech signals, as well as the non-linear relationship between noise and speech.
Section 3: Datasets and Evaluation Metrics
To train and evaluate DL models for speech enhancement, large datasets of noisy and clean speech samples are required. The most commonly used datasets include the Noise-Robust Speech (NRS) database, the Cocktail Party (CP) dataset, and the Wall Street Journal (WSJ) corpus. Evaluation metrics such as signal-to-noise ratio (SNR), intelligibility, and perceptual evaluation of speech quality (PESQ) are used to measure the performance of DL models in speech enhancement tasks.
Section 4: Advances and Challenges in Speech Enhancement
Despite the promising results achieved by DL-based speech enhancement models, there are still several challenges that need to be addressed, including the lack of diverse and representative datasets, the difficulty in modeling non-stationary noise environments, and the requirement for careful tuning of hyperparameters. Future research should focus on addressing these challenges to improve the generalization ability and robustness of DL models for speech enhancement.
Conclusion
In conclusion, this survey provides a comprehensive overview of the current state-of-the-art in DL-based speech enhancement. The key findings include the effectiveness of CNNs and RNNs in modeling speech features, the importance of large datasets for training and evaluation, and the need for further research to overcome the challenges associated with speech enhancement tasks. As the field continues to evolve, we can expect DL-based speech enhancement models to improve in accuracy and robustness, leading to better performance in real-world applications.
