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Summary:
When computing the MVDR beamforming weights using the power iteration method, the PSD matrix of noise can be applied with diagonal loading to improve the robustness. This is also applicable to computing the RTF matrix (See https://github.com/espnet/espnet/blob/master/espnet2/enh/layers/beamformer.py#L614 as an example). This also aligns with current `torchaudio.transforms.MVDR` module to keep the consistency.

This PR adds the `diagonal_loading` argument with `True` as default value to `torchaudio.functional.rtf_power`.

Pull Request resolved: https://github.com/pytorch/audio/pull/2369

Reviewed By: carolineechen

Differential Revision: D36204130

Pulled By: nateanl

fbshipit-source-id: 93a58d5c2107841a16c4e32f0c16ab0d6b2d9420

Summary:
This PR adds ``apply_beamforming`` method to ``torchaudio.functional``.
The method employs the beamforming weight to the multi-channel noisy spectrum to obtain the single-channel enhanced spectrum.
The input arguments are the complex-valued beamforming weight Tensor and the multi-channel noisy spectrum.

Pull Request resolved: https://github.com/pytorch/audio/pull/2232

Reviewed By: mthrok

Differential Revision: D34474561

Pulled By: nateanl

fbshipit-source-id: 2910251a8f111e65375dfb50495b6a415113f06d

Summary:
This PR adds ``rtf_power`` method to ``torchaudio.functional``.
The method computes the relative transfer function (RTF) or the steering vector by [the power iteration method](https://onlinelibrary.wiley.com/doi/abs/10.1002/zamm.19290090206).
[This paper](https://arxiv.org/pdf/2011.15003.pdf) describes the power iteration method in English.
The input arguments are the complex-valued power spectral density (PSD) matrix of the target speech, PSD matrix of noise, int or one-hot Tensor to indicate the reference channel, number of iterations, respectively.

Pull Request resolved: https://github.com/pytorch/audio/pull/2231

Reviewed By: mthrok

Differential Revision: D34474503

Pulled By: nateanl

fbshipit-source-id: 47011427ec4373f808755f0e8eff1efca57655eb

Summary:
This PR adds `rtf_evd` method to `torchaudio.functional`.
The method computes the relative transfer function (RTF) or the steering vector by eigenvalue decomposition.
The input argument is the power spectral density (PSD) matrix of the target speech.

Pull Request resolved: https://github.com/pytorch/audio/pull/2230

Reviewed By: mthrok

Differential Revision: D34474188

Pulled By: nateanl

fbshipit-source-id: 888df4b187608ed3c2b7271b34d2231cdabb0134

Summary:
This PR adds ``mvdr_weights_rtf`` method to ``torchaudio.functional``.
It computes the MVDR weight matrix based on the solution that applies relative transfer function (RTF). See [the paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.725.673&rep=rep1&type=pdf) for the reference.
The input arguments are the complex-valued RTF Tensor of the target speech, power spectral density (PSD) matrix of noise, int or one-hot Tensor to indicate the reference channel, respectively.

Pull Request resolved: https://github.com/pytorch/audio/pull/2229

Reviewed By: mthrok

Differential Revision: D34474119

Pulled By: nateanl

fbshipit-source-id: 2d6f62cd0858f29ed6e4e03c23dcc11c816204e2

Summary:
This PR adds ``mvdr_weights_souden`` method to ``torchaudio.functional``.
It computes the MVDR weight matrix based on the solution proposed by [``Souden et, al.``](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.725.673&rep=rep1&type=pdf).
The input arguments are the complex-valued power spectral density (PSD) matrix of the target speech, PSD matrix of noise, int or one-hot Tensor to indicate the reference channel, respectively.

Pull Request resolved: https://github.com/pytorch/audio/pull/2228

Reviewed By: mthrok

Differential Revision: D34474018

Pulled By: nateanl

fbshipit-source-id: 725df812f8f6e6cc81cc37e8c3cb0da2ab3b74fb

Summary:
This PR adds ``psd`` method to ``torchaudio.functional``.
It computes the power spectral density (PSD) matrix of the complex-valued spectrum.
The method also supports normalization of Time-Frequency mask.

Pull Request resolved: https://github.com/pytorch/audio/pull/2227

Reviewed By: mthrok

Differential Revision: D34473908

Pulled By: nateanl

fbshipit-source-id: c1cfc584085d77881b35d41d76d39b26fca1dda9

Summary:
Adds parameter `p` to `TimeMasking` to allow for enforcing an upper bound on the proportion of time steps that it can mask. This behavior is consistent with the specifications provided in the SpecAugment paper (https://arxiv.org/abs/1904.08779).

Pull Request resolved: https://github.com/pytorch/audio/pull/2090

Reviewed By: carolineechen

Differential Revision: D33344772

Pulled By: hwangjeff

fbshipit-source-id: 6ff65f5304e489fa1c23e15c3d96b9946229fdcf

Summary:
Pull Request resolved: https://github.com/pytorch/audio/pull/2096

run: `arc lint --apply-patches --paths-cmd 'hg files -I "./**/*.py"'`

Reviewed By: mthrok

Differential Revision: D33297351

fbshipit-source-id: 7bf5956edf0717c5ca90219f72414ff4eeaf5aa8

Following the plan #1337, this commit drops the support for pseudo complex type from `F.phase_vocoder` and `T.TimeStretch`.

* Add basic filtfilt implementation

* Add filtfilt to functional package; add tests
Co-authored-by: V G <vladislav.goncharenko@phystech.edu>

- Renamed torchaudio.functional.create_fb_matrix to torchaudio.functional.melscale_fbanks.
- Added interface with a warning for create_fb_matrix

* Remove `class FunctionalComplex` header accidentally re-introduced in #1490 

It was reported in #1478 that spectrogram masking operations were done in-place and modified the original input tensors. This PR fixes this behavior and adds tests to ensure that the input tensor is not changed.

- Put functional test logic into one place, `functional_impl.py`
- Tidy imports

Merges lfilter and spectrogram classes together in the common implementation and modifies the cpu and gpu test definitions accordingly

1. `F.phase_vocoder` accepts Tensor with complex dtype.
    * The implementation path has been updated from #758 so that they share the same code path by internally converting the input Tensor to complex dtype and performing all the operation on top of it.
    * Adopted `torch.polar` for simpler Tensor generation from magnitude and angle.
2. Updated tests
    * librosa compatibility test for complex dtype and pseudo complex dtype
        * Extracted the output shape check test and moved it to functional so that it will be tested on all the combination of `{CPU | CUDA} x {complex64 | complex128}`
    * TorchScript compatibility test for `F.phase_vocoder` and `T.TimeStretch`.
    * batch consistency test for `T.TimeStretch`.

We have been running unit test with editable installation. (i.e. `python setup.py develop`), with which we missed issues like #842. 

This CC makes installation in CI non-editable, and change test directory structure so that the source code will not shadow the installed version of `torchaudio`. With simple `pytest test`, `pytest` modifies `sys.path` and prepend checked out repository, which shadows the installed version.

To remedy this, the whole test suites has been moved from `./test` to `./test/torchaudio_unittest`. This adds nice module structure to our test code and we can do absolute import in each test module, which makes it possible again to run test with `python -m unittest torchaudio_unittest/XXX.py`

This change does not affect the regular development process (`python setup.py develop` && `pytest test`)

`type` used in `common_utils` generates test class definition in `common_utils` and
this modifies the module state after it's imported. This is anti-pattern.
This PR get rid of the related utility functions and define test suite manually.