A Flexible Online Framework for Projection-Based STFT Phase Retrieval

TL;DR

This paper introduces a flexible online framework for projection-based STFT phase retrieval, enabling real-time speech signal reconstruction with improved performance over traditional offline methods.

Contribution

It extends RTISI into a versatile online framework allowing easy implementation of various iterative projection algorithms for real-time speech processing.

Findings

Online algorithms outperform RTISI in reconstruction quality.

Framework enables real-time processing for multiple phase retrieval methods.

Significant performance improvements demonstrated on speech signals.

Abstract

Several recent contributions in the field of iterative STFT phase retrieval have demonstrated that the performance of the classical Griffin-Lim method can be considerably improved upon. By using the same projection operators as Griffin-Lim, but combining them in innovative ways, these approaches achieve better results in terms of both reconstruction quality and required number of iterations, while retaining a similar computational complexity per iteration. However, like Griffin-Lim, these algorithms operate in an offline manner and thus require an entire spectrogram as input, which is an unrealistic requirement for many real-world speech communication applications. We propose to extend RTISI -- an existing online (frame-by-frame) variant of the Griffin-Lim algorithm -- into a flexible framework that enables straightforward online implementation of any algorithm based on iterative projections. We further employ this framework to implement online variants of the fast Griffin-Lim algorithm, the accelerated Griffin-Lim algorithm, and two algorithms from the optics domain. Evaluation results on speech signals show that, similarly to the offline case, these algorithms can achieve a considerable performance gain compared to RTISI.