DriftRec: Adapting Diffusion Models to Blind JPEG Restoration.

In this work, we utilize the high-fidelity generation abilities of diffusion models to solve blind JPEG restoration at high compression levels. We propose an elegant modification of the forward stochastic differential equation of diffusion models to adapt them to this restoration task and name our method DriftRec. Comparing DriftRec against an L regression baseline with the same network architecture and state-of-the-art techniques for JPEG restoration, we show that our approach can escape the tendency of other methods to generate blurry images, and recovers the distribution of clean images significantly more faithfully.

Phase-aware deep speech enhancement: It's all about the frame length.

Algorithmic latency in speech processing is dominated by the frame length used for Fourier analysis, which in turn limits the achievable performance of magnitude-centric approaches. As previous studies suggest the importance of phase grows with decreasing frame length, this work presents a systematic study on the contribution of phase and magnitude in modern deep neural network (DNN)-based speech enhancement at different frame lengths. Results indicate that DNNs can successfully estimate phase when using short frames, with similar or better overall performance compared to using longer frames.

Efficient Joint Estimation of Tracer Distribution and Background Signals in Magnetic Particle Imaging Using a Dictionary Approach.

Background signals are a primary source of artifacts in magnetic particle imaging and limit the sensitivity of the method since background signals are often not precisely known and vary over time. The state-of-the art method for handling background signals uses one or several background calibration measurements with an empty scanner bore and subtracts a linear combination of these background measurements from the actual particle measurement. This approach yields satisfying results in case that the background measurements are taken in close proximity to the particle measurement and when the background signal drifts linearly.

A Survey on Probabilistic Models in Human Perception and Machines.

Extracting information from noisy signals is of fundamental importance for both biological and artificial perceptual systems. To provide tractable solutions to this challenge, the fields of human perception and machine signal processing (SP) have developed powerful computational models, including Bayesian probabilistic models. However, little true integration between these fields exists in their applications of the probabilistic models for solving analogous problems, such as noise reduction, signal enhancement, and source separation.

Evaluation of combined dynamic compression and single channel noise reduction for hearing aid applications.

Objective: Single-channel noise reduction (SCNR) and dynamic range compression (DRC) are important elements in hearing aids. Only relatively few studies have addressed interaction effects and typically used real hearing aids with limited knowledge about the integrated algorithms. Here the potential benefit of different combinations and integration of SCNR and DRC was systematically assessed.

Comparison of single-microphone noise reduction schemes: can hearing impaired listeners tell the difference?

Objective: The perceived qualities of nine different single-microphone noise reduction (SMNR) algorithms were to be evaluated and compared in subjective listening tests with normal hearing and hearing impaired (HI) listeners.

Design: Speech samples added with traffic noise or with party noise were processed by the SMNR algorithms. Subjects rated the amount of speech distortions, intrusiveness of background noise, listening effort and overall quality, using a simplified MUSHRA (ITU-R, 2003 ) assessment method.

An evaluation of the perceptual quality of phase-aware single-channel speech enhancement.

For the enhancement of single-channel speech corrupted by acoustic noise, recently short-time Fourier transform domain clean speech estimators were proposed that incorporate prior information about the clean speech spectral phase. Instrumental measures predict quality improvements for the phase-aware estimators over their conventional phase-blind counterparts. In this letter, these predictions are verified by means of listening experiments.

Comparing Binaural Pre-processing Strategies II: Speech Intelligibility of Bilateral Cochlear Implant Users.

Several binaural audio signal enhancement algorithms were evaluated with respect to their potential to improve speech intelligibility in noise for users of bilateral cochlear implants (CIs). 50% speech reception thresholds (SRT50) were assessed using an adaptive procedure in three distinct, realistic noise scenarios. All scenarios were highly nonstationary, complex, and included a significant amount of reverberation.

Comparing Binaural Pre-processing Strategies I: Instrumental Evaluation.

In a collaborative research project, several monaural and binaural noise reduction algorithms have been comprehensively evaluated. In this article, eight selected noise reduction algorithms were assessed using instrumental measures, with a focus on the instrumental evaluation of speech intelligibility. Four distinct, reverberant scenarios were created to reflect everyday listening situations: a stationary speech-shaped noise, a multitalker babble noise, a single interfering talker, and a realistic cafeteria noise.