{"title":"Deep Preprocessing Method for Speech Restoration in Parametric Array Loudspeakers via Time-Frequency Domain Modeling","authors":"Wenyao Ma;Yunxi Zhu;Jun Yang","doi":"10.1109/LSP.2025.3609247","DOIUrl":null,"url":null,"abstract":"The parametric array loudspeaker inherently introduces baseband distortions in directional sound applications due to the nonlinear process in air. Recently, DNNs have been used to model this forward process and to generate preprocessed signals for distortion-free speech restoration. However, when trained on real-world audio, the preprocessing network can exploit weaknesses in the forward model, producing adversarial outputs. To address it, we propose a reorganization strategy for the two-stage framework, comprising a causal TF-GridNet for preprocessed signal generation and a modified time-frequency (T-F) domain differential Volterra Filter (DiffVF) as the forward model. The causal TF-GridNet estimates real and imaginary components using a T-F band-split mechanism. The modified forward model integrates the second-order difference and kernel convolution operations of the original time-domain version into the T-F domain, preserving interpretability while stabilizing training. A refined <inline-formula><tex-math>$N$</tex-math></inline-formula>th-order equalization, based on the T-F domain DiffVF model, is implemented as a competitive baseline. Simulated and real-world experiments demonstrate state-of-the-art reconstruction performance of the proposed method across various objective metrics.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"3720-3724"},"PeriodicalIF":3.9000,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Signal Processing Letters","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11159167/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The parametric array loudspeaker inherently introduces baseband distortions in directional sound applications due to the nonlinear process in air. Recently, DNNs have been used to model this forward process and to generate preprocessed signals for distortion-free speech restoration. However, when trained on real-world audio, the preprocessing network can exploit weaknesses in the forward model, producing adversarial outputs. To address it, we propose a reorganization strategy for the two-stage framework, comprising a causal TF-GridNet for preprocessed signal generation and a modified time-frequency (T-F) domain differential Volterra Filter (DiffVF) as the forward model. The causal TF-GridNet estimates real and imaginary components using a T-F band-split mechanism. The modified forward model integrates the second-order difference and kernel convolution operations of the original time-domain version into the T-F domain, preserving interpretability while stabilizing training. A refined $N$th-order equalization, based on the T-F domain DiffVF model, is implemented as a competitive baseline. Simulated and real-world experiments demonstrate state-of-the-art reconstruction performance of the proposed method across various objective metrics.
期刊介绍:
The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.