PiENet

Pitch estimation network (PiENet) for noise-robust neural F0 estimation of speech signals. The best performing model from [1] ('GTE-AUG') supplied as a pre-trained model. The model was trained using additional and convolutional noise augmentation, as well as vocoder-based ground truth enhancement (see publication for further details).

Article found in: IEEE Xplore, ResearchGate

Licence

Distributed under the Apache 2.0 license. See LICENSE for further details.

Dependencies

• Tensorflow (tested with version 1.10)
• Numpy
• Scipy

How to use pre-trained model:

The pre-trained model is trained for speech sampled at 16 kHz, with 10 ms frame shift and a F0 range of 50 to 500 Hz.

Method 1:

• python generate.py : Perform pitch estimation to the .wav files specified in the text file test_wavs.scp and outputs the .f0 files to the default folder located at ./f0/.

Method 2:

• python generate.py input_file.wav : Perform pitch estimation to input_file.wav, output to ./f0/.

• python generate.py input_list.scp : Perform pitch estimation to all files specified in the text file input_list.scp, output to ./f0/.

• python generate.py input_dir : Perform pitch estimation to all .wav files found in the folder input_dir, output to ./f0/.

Method 3:

• python generate.py [input_file/input_list/input_dir] target_dir : Same options as Method 2, output to target_dir.

How to train your own model:

• Supply list of train data files in train_wavs.scp for input data, and the corresponding target F0s in train_f0s.scp (datatype raw float32 for F0 files). Note that the number of frames in the F0 files must match the present method's framing convention (Nframes = ceil(wav_length/hop), signal zero-padded with winlen/2 samples from start and beginning).

• If you want to use additive noise augmentation with sampled noise from a database, supply the file paths in noiselist.scp and edit the code in train.py line 54 to noise_samples = augmentation.load_noise_samples(noise_wav_scp=noiselist.scp).

• For vocoder-based augmentation techniques, you need to download and the vocoder of your choice. The GlottDNN vocoder used in [1] can be found in https://github.com/ljuvela/GlottDNN. For ground truth enhancement, process the train wavs offline with the vocoder. The code for diversity augmentation is not provided in the current release of the method.

Reference:

[1] M. Airaksinen, L. Juvela, P. Alku and O. Räsänen: "Data augmentation strategies for neural F0 estimation", Proc. ICASSP 2019

Available: IEEE Xplore, ResearchGate