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Perceptual Quality Loss or Loss of Perceptual Quality?
Danilo de Oliveira, Tal Peer, Maurício do V. M. da Costa +1
Contemporary deep speech enhancement (SE) models are often trained with specific auxiliary terms in the loss function as a way to improve their performance in terms of perceptual m…
Livin' on a Prior: Likelihood Score Approximation for Inverse Problems
Rostislav Makarov, Tal Peer, Danilo de Oliveira +1
Generative models have found great success as data-driven methods of solving inverse problems. Two popular approaches work either by combining a pretrained generative prior with a…
Directivity-Conditioned Low-Latency Neural Filtering for Speech Enhancement in Hearing Aids
Lennart Uphaus, André Merboldt, Markus Hofbauer +1
Latest advances in neural directional filtering show exceptional results in adapting the direction and shape of directivity patterns during the inference phase. However, in the exi…
Test-time adaptation for speech enhancement with an autoregressive speech prior
Sofiene Kammoun, Simon Leglaive, Xavier Alameda-Pineda +1
Test-time adaptation (TTA) offers a promising direction for improving speech enhancement models under mismatched acoustic conditions, without requiring access to labeled target dat…
EffVOC: Low-Delay Efficient Speech Waveform Reconstruction from Spectral Representations Without Phase
Renzheng Shi, Simon Welker, Timo Gerkmann +1
The Griffin-Lim algorithm has been a seminal contribution for phase reconstruction from amplitude spectrograms, however, requiring (infinitely) high algorithmic delay. Its low-dela…
Repurposing a Speech Classifier for Guided Diffusion-Based Speech Generation
Rostislav Makarov, Timo Gerkmann
Classifier guidance is a way to control diffusion generation by using a noise-conditioned classifier to steer the sampling process toward a target class. One drawback of classifier…
Your U-Net Dereverberation Model is Secretly an RIR Encoder
Sina Khanagha, Timo Gerkmann
In this work, we analyze the ability of NCSN++ U-Net based audio dereverberation models to capture global room characteristics in their intermediate representations. Through an emp…
Too Good to Be True: A Study on Modern Automatic Speech Recognition for the Evaluation of Speech Enhancement
Danilo de Oliveira, Tal Peer, Timo Gerkmann
Speech enhancement (SE) systems are typically evaluated using a variety of instrumental metrics. The use of automatic speech recognition (ASR) systems to evaluate SE performance is…
A Fast Solver for Interpolating Stochastic Differential Equation Diffusion Models for Speech Restoration
Bunlong Lay, Timo Gerkmann
Diffusion Probabilistic Models (DPMs) are a well-established class of diffusion models for unconditional image generation, while SGMSE+ is a well-established conditional diffusion…
Bone-conduction Guided Multimodal Speech Enhancement with Conditional Diffusion Models
Sina Khanagha, Bunlong Lay, Timo Gerkmann
Single-channel speech enhancement models face significant performance degradation in extremely noisy environments. While prior work has shown that complementary bone-conducted spee…