SPSDASJun 9, 2021

Time-Frequency Phase Retrieval for Audio -- The Effect of Transform Parameters

arXiv:2106.05148v11 citations
Originality Synthesis-oriented
AI Analysis

This work addresses the lack of systematic evaluation in audio phase retrieval, providing practical guidelines for parameter optimization, though it is incremental as it builds on existing methods.

The authors systematically evaluated how short-time Fourier transform parameters affect phase retrieval performance for audio, finding that redundancy and time-frequency ratio significantly improve quality, while audio content and window type have smaller effects, with each algorithm requiring different optimal parameters.

In audio processing applications, phase retrieval (PR) is often performed from the magnitude of short-time Fourier transform (STFT) coefficients. Although PR performance has been observed to depend on the considered STFT parameters and audio data, the extent of this dependence has not been systematically evaluated yet. To address this, we studied the performance of three PR algorithms for various types of audio content and various STFT parameters such as redundancy, time-frequency ratio, and the type of window. The quality of PR was studied in terms of objective difference grade and signal-to-noise ratio of the STFT magnitude, to provide auditory- and signal-based quality assessments. Our results show that PR quality improved with increasing redundancy, with a strong relevance of the time-frequency ratio. The effect of the audio content was smaller but still observable. The effect of the window was only significant for one of the PR algorithms. Interestingly, for a good PR quality, each of the three algorithms required a different set of parameters, demonstrating the relevance of individual parameter sets for a fair comparison across PR algorithms. Based on these results, we developed guidelines for optimizing STFT parameters for a given application.

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