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version](https://badge.fury.io/py/torchsubband.svg)](https://badge.fury.io/py/torchsubband)\n\n# torchsubband\n\nThis's a package for subband decomposition. \n\nIt can transform waveform into three kinds of revertable subband feature representations, which are potentially useful features for music source separation or similar tasks.\n\n[![488zR0.png](https://z3.ax1x.com/2021/09/19/488zR0.png)](https://imgtu.com/i/488zR0)\n\n## Usage\n\nInstallation\n```shell\npip install torchsubband\n```\n\nA simple example: \n\n```python\nfrom torchsubband import SubbandDSP\nimport torch\n\n# nn.Module\nmodel = SubbandDSP(subband=2) # You can choose 1,2,4, or 8 \nbatchsize=3 # any int number\nchannel=1 # any int number\nlength = 44100*2 # any int number\ninput = torch.randn((batchsize,channel,length))\n\n# Get subband waveform\nsubwav = model.wav_to_sub(input)\nreconstruct_1 = model.sub_to_wav(subwav,length=length)\n\n# Get subband magnitude spectrogram\nsub_spec,cos,sin = model.wav_to_mag_phase_sub_spec(input)\nreconstruct_2 = model.mag_phase_sub_spec_to_wav(sub_spec,cos,sin,length=length)\n\n# Get subband complex spectrogram\nsub_complex_spec = model.wav_to_complex_sub_spec(input)\nreconstruct_3 = model.complex_sub_spec_to_wav(sub_complex_spec,length=length)\n```\n\n## Reconstruction loss\n\nThe following table shows the reconstruction quality. We tried a set of audio to conduct subband decomposition and reconstruction.\n\n\n| Subbands |  L1loss   | PESQ  | SiSDR|\n| :----: | :----: | :----: | :----:\n| 2 | 1e-6  | 4.64 | 61.8 |\n| 4 | 1e-6  | 4.64 | 58.9 |\n| 8 | 5e-5  | 4.64 | 58.2 |\n\nYou can also test this program by running the following test script. It will give you some evaluation output.\n\n```python\nfrom torchsubband import test\ntest()\n```\n\n## Citation\n\nIf you find our code useful for your research, please consider citing:\n\n```bib\n    @misc{liu2021cwspresunet,\n        title={CWS-PResUNet: Music Source Separation with Channel-wise Subband Phase-aware ResUNet},\n        author={Haohe Liu and Qiuqiang Kong and Jiafeng Liu},\n        year={2021},\n        eprint={2112.04685},\n        archivePrefix={arXiv},\n        primaryClass={cs.SD}\n    }\n```\n\n```bib\n    @inproceedings{Liu2020,   \n      author={Haohe Liu and Lei Xie and Jian Wu and Geng Yang},   \n      title={{Channel-Wise Subband Input for Better Voice and Accompaniment Separation on High Resolution Music}},   \n      year=2020,   \n      booktitle={Proc. Interspeech 2020},   \n      pages={1241--1245},   \n      doi={10.21437/Interspeech.2020-2555},   \n      url={http://dx.doi.org/10.21437/Interspeech.2020-2555}   \n    }\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhaoheliu%2Ftorchsubband","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhaoheliu%2Ftorchsubband","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhaoheliu%2Ftorchsubband/lists"}