Phase retrieval with Bregman divergences and application to audio signal recovery

TL;DR

This paper introduces a novel phase retrieval approach for audio signals using Bregman divergences instead of traditional quadratic loss, leading to improved audio recovery especially in noisy environments.

Contribution

It formulates phase retrieval as a minimization problem with Bregman divergences and develops two algorithms for optimized audio signal recovery.

Findings

Bregman divergence-based methods outperform quadratic loss in noisy conditions.

Proposed algorithms effectively recover audio signals from spectrograms.

Methods demonstrate potential for enhanced audio restoration applications.

Abstract

Phase retrieval (PR) aims to recover a signal from the magnitudes of a set of inner products. This problem arises in many audio signal processing applications which operate on a short-time Fourier transform magnitude or power spectrogram, and discard the phase information. Recovering the missing phase from the resulting modified spectrogram is indeed necessary in order to synthesize time-domain signals. PR is commonly addressed by considering a minimization problem involving a quadratic loss function. In this paper, we adopt a different standpoint. Indeed, the quadratic loss does not properly account for some perceptual properties of audio, and alternative discrepancy measures such as beta-divergences have been preferred in many settings. Therefore, we formulate PR as a new minimization problem involving Bregman divergences. Since these divergences are not symmetric with respect to their two input arguments in general, they lead to two different formulations of the problem. To optimize the resulting objective, we derive two algorithms based on accelerated gradient descent and alternating direction method of multipliers. Experiments conducted on audio signal recovery from spectrograms that are either exact or estimated from noisy observations highlight the potential of our proposed methods for audio restoration. In particular, leveraging some of these Bregman divergences induce better performance than the quadratic loss when performing PR from spectrograms under very noisy conditions.