unknown_number_source_separation

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unknown_number_source_separation

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Source separation of underwater acoustic radiated noise signals from ships with unknown numbers of signals. Using keras 2.2.4 with tensorflow-gpu 1.12.0 backend.

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#  unknown_number_source_separation
Underwater single-channel acoustic source separation with unknown numbers using autoencoder nerual networks. Using keras-gpu 2.2.4 with tensorflow-gpu 1.12.0 backend.  
#  How to cite this work
This is the official code of the blow article.
Please cite this work in your publications as :  
<pre>
@misc{sun2022source,
      title={Source Separation of Unknown Numbers of Single-Channel Underwater Acoustic Signals Based on Autoencoders}, 
      author={Qinggang Sun and Kejun Wang},
      year={2022},
      eprint={2207.11749},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
} 
</pre>
#  How to use
<pre>
1. Download and organize data files as "data_dir_tree.md".
    The official website of the shipsEar database is https://atlanttic.uvigo.es/underwaternoise/ .
    Users may contact the author David Santos Domínguez through email dsantos@gts.uvigo.es to get the database.
2. Install dependent packages in the "requirements.txt" file.
3. Run
	prepare_data_shipsear_recognition_mix_s0tos3.py
	recognition_mix_shipsear_s0tos3_preprocess.py
	prepare_data_shipsear_separation_mix_s0tos3.py
	separation_mix_shipsear_s0tos3_preprocess.py
   to preprocess datas.
4. Experiments:
	(0) Known numbers of source separation with multiple autoencoders.
		train_separation_multiple_autoencoder.py
	(1) Known numbers of source separation with multiple output autoencoders.
		train_separation_known_number.py
	(2) Algorithm 1.
		train_separation_one_autoencoder.py
	(3) Algorithm 2.
		train_single_source_autoencoder_ns.py
		train_single_source_autoencoder_ns_search_encoded_dense.py
	(4) Algorithm 3.
		train_single_source_autoencoder_ns.py
		train_separation_one_autoencoder_freeze_decoder.py
	(5) FUSS.
		train_separation_fuss.py
	(6) CBIR in (Kim, 2023).
		train_separation_kim.py
</pre>
#  Reference
Please cite the original work as :  
[Generative sourceseparation with GANs](https://github.com/ycemsubakan/sourceseparation_misc#generative-sourceseparation-with-gans)  
[Two-Step Sound Source Separation: Training on Learned Latent Targets](https://github.com/etzinis/two_step_mask_learning#two-step-sound-source-separation-training-on-learned-latent-targets)  
[Dual-Path RNN for Single-Channel Speech Separation](https://github.com/sp-uhh/dual-path-rnn#dual-path-rnn-for-single-channel-speech-separation-in-keras-tensorflow2)  
[Dual-Path RNN for Single-Channel Speech Separation (in Keras-Tensorflow2)](https://github.com/sp-uhh/dual-path-rnn)  
[Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation](https://github.com/naplab/Conv-TasNet#conv-tasnet-surpassing-ideal-timefrequency-magnitude-masking-for-speech-separation)  
[conv-tasnet](https://github.com/helianvine/conv-tasnet)  
[Deep Learning for Blind Source Separation](https://github.com/ishine/tf2-ConvTasNet)  
[Conv-TasNet](https://github.com/kaparoo/Conv-TasNet)  
[Conv-TasNet](https://github.com/shunyaoshih/Conv-TasNet)  
[Wave-U-Net](https://github.com/f90/Wave-U-Net)  
[Wave-U-net-TF2](https://github.com/satvik-venkatesh/Wave-U-net-TF2)  
[Sound Separation](https://github.com/google-research/sound-separation)  
[bsseval](https://github.com/sigsep/bsseval)

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