BAPEN: Towards Versatile Audio Phase Retrieval

Audio phase retrieval aims to reconstruct phase from the given magnitude and obtain the time-domain audio waveform. While deep learning techniques have promoted the development of this area, existing deep neural network (DNN)-based methods usually suffer from some inherent problems like limited generalization capability to different audio types, failing to adapt to different sampling rates, and inflexibility for varying computational complexity during the inference stage, which heavily hinder the development of the filed. To tackle these challenges, in this paper, we introduce a novel phase task estimation task called versatile auido phase retrieval and a Band-Aware Phase Estimation Network (BAPEN ) is proposed. Specifically, we first collect and establish a new benchmark for the task, which encompasses speech, sound effects, and music and the total duration is around 414 hours. Besides, a sub-band oriented framework is proposed, which involves hierarchical sub-band encoding/decoding and a dual-path network structure is specially devised for efficient narrow- and cross-band modeling, respectively. Furthermore, to enable dynamic control over the inference cost, we propose a simple yet effective sampling strategy for network depth augmentation during training. Both objective and subject results validate the promising performance of the BAPEN while possessing more flexible application ranges. Audio samples are available on: https://lingling-dai.github.io/BAPEN/.