Editing State-of-the-art (section)

==== Zegers, J. (2019). CNN-LSTM models for multi-speaker source separation using Bayesian hyperparameter optimization. arXiv preprint arXiv:1912.09254. ====
'''Summary:''' This paper explores the use of Bayesian hyperparameter optimization for parallel CNN-LSTM models in the task of multi-speaker source separation (MSSS). Experiments were conducted with mixtures from the WSJ0 corpus and found that parallel CNN-LSTM models performed better than individual CNN or LSTM models. 

'''Research Question (RQ):''' How does Bayesian hyperparameter optimization affect the performance of parallel CNN-LSTM models in multi-speaker source separation?

'''Hypothesis:''' The hypothesis was that the Bayesian optimization technique would find a better hyperparameter set that allows the parallel CNN-LSTM model to outperform individual CNNs or LSTMs in MSSS.

'''Conclusion:''' The study concluded that models with more trainable parameters in the LSTM portion performed better and that parallel CNN-LSTM models with Bayesian hyperparameter optimization outperformed the other models tested.

'''Critical Observations:''' The LSTM part of the model was crucial for performance, and bidirectional LSTMs performed better than unidirectional ones. Also, the study noted that more trainable parameters in the LSTM were generally preferred.

'''Relevance:''' This research is relevant for advancements in speech processing, specifically in improving source separation techniques which is a foundational task in many audio processing applications.