Long-Frame-Shift Neural Speech Phase Prediction with Spectral Continuity Enhancement and Interpolation Error Compensation
In our paper,
we proposed a novel speech phase prediction method which predicts long-frame-shift wrapped phase spectra from amplitude spectra by combining neural networks and signal processing knowledges.
We provide our implementation as open source in this repository.
Abstract : Speech phase prediction, which is a significant research focus in the field of signal processing, aims to recover speech phase spectra from amplitude-related features. However, existing speech phase prediction methods are constrained to recovering phase spectra with short frame shifts, which are considerably smaller than the theoretical upper bound required for exact waveform reconstruction of short-time Fourier transform (STFT). To tackle this issue, we present a novel long-frame-shift neural speech phase prediction (LFS-NSPP) method which enables precise prediction of long-frame-shift phase spectra from long-frame-shift log amplitude spectra. The proposed method consists of three stages: interpolation, prediction and decimation. The short-frame-shift log amplitude spectra are first constructed from long-frame-shift ones through frequency-by-frequency interpolation to enhance the spectral continuity, and then employed to predict short-frame-shift phase spectra using an NSPP model, thereby compensating for interpolation errors. Ultimately, the long-frame-shift phase spectra are obtained from short-frame-shift ones through frame-by-frame decimation. Experimental results show that the proposed LFS-NSPP method can yield superior quality in predicting long-frame-shift phase spectra than the original NSPP model and other signal-processing-based phase estimation algorithms.
Visit our demo website for audio samples.
torch==1.8.1+cu111
numpy==1.21.6
librosa==0.9.1
tensorboard==2.8.0
soundfile==0.10.3
matplotlib==3.1.3
For training, write the list paths of training set and validation set to input_training_wav_list and input_validation_wav_list in config.json, respectively.
For generation, we provide two ways to read data:
(1) set test_input_log_amp_dir to 0 in config.json and write the test set waveform path to test_input_wavs_dir in config.json, the generation process will load the waveform, extract the log amplitude spectra, predict the phase spectra and reconstruct the waveform;
(2) set test_input_log_amp_dir to 1 in config.json and write the log amplitude spectra (size is (n_fft/2+1)*frames) path to test_input_log_amp_dir in config.json, the generation process will dierctly load the log amplitude spectra, predict the phase spectra and reconstruct the waveform.
In config.json, hop_size is the long-frame-shift (unit: sample points) and transit_hop_size is the short-frame-shift (unit: sample points).
Run using GPU:
CUDA_VISIBLE_DEVICES=0 python train.py
Using TensorBoard to monitor the training process:
tensorboard --logdir=cp_NSPP/logs
Write the checkpoint path to checkpoint_file_load in config.json.
Run using GPU:
CUDA_VISIBLE_DEVICES=0 python generation.py
Run using CPU:
CUDA_VISIBLE_DEVICES=CPU python generation.py
@article{ai2023long,
title={Long-frame-shift Neural Speech Phase Prediction with Spectral Continuity Enhancement and Interpolation Error Compensation},
author={Ai, Yang and Lu, Ye-Xin and Ling, Zhen-Hua},
journal={IEEE Signal Processing Letters},
year={2023}
}