37 accepted papers
Machine learning approaches for speech enhancement are becoming increasingly expressive, enabling ever more powerful modifications of input signals. In this paper, we demonstrate that this expressiveness introduces a vulnerability: advanced speech enhancement models can be susceptible to adversarial…
We propose FlowDec, a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from…
We present a head-related transfer function (HRTF) estimation method which relies on a data-driven prior given by a score-based diffusion model. The HRTF is estimated in reverberant environments using natural excitation signals, e.g. human speech. The impulse response of the room is estimated along…
Generative speech enhancement has recently shown promising advancements in improving speech quality in noisy environments. Multiple diffusion-based frameworks exist, each employing distinct training objectives and learning techniques. This paper aims to explain the differences between these framewor…
Due to their robustness and flexibility, neural-driven beamformers are a popular choice for speech separation in challenging environments with a varying amount of simultaneous speakers alongside noise and reverberation. Time-frequency masks and relative directions of the speakers regarding a fixed s…
Several recent contributions in the field of iterative STFT phase retrieval have demonstrated that the performance of the classical Griffin-Lim method can be considerably improved upon. By using the same projection operators as Griffin-Lim, but combining them in innovative ways, these approaches ach…
Much research effort is being applied to the task of compressing the knowledge of self-supervised models, which are powerful, yet large and memory consuming. Existing works generally distill internal features of self-supervised Transformer models. In this work, aiming at more flexibility in the desi…
Speech emotion conversion is the task of converting the expressed emotion of a spoken utterance to a target emotion while preserving the lexical content and speaker identity. While most existing works in speech emotion conversion rely on acted-out datasets and parallel data samples, in this work we…
In this work, we demonstrate that the ptychographic phase problem can be solved in a live fashion during scanning, while data is still being collected. We propose a generally applicable modification of the widespread projection-based algorithms such as Error Reduction (ER) and Difference Map (DM). T…
Diffusion models have shown promising results in single-channel speech enhancement, using a task-adapted diffusion process for the conditional generation of clean speech given a noisy mixture. However, at test time, the neural network used for score estimation is called multiple times to solve the i…
Diffusion-based generative models have had a high impact on the computer vision and speech processing communities these past years. Besides data generation tasks, they have also been employed for data restoration tasks like speech enhancement and dereverberation. While discriminative models have tra…
Diffusion probabilistic models have been recently used in a variety of tasks, including speech enhancement and synthesis. As a generative approach, diffusion models have been shown to be especially suitable for imputation problems, where missing data is generated based on existing data. Phase retrie…
Human-robot interaction relies on a noise-robust audio processing module capable of estimating target speech from audio recordings impacted by environmental noise, as well as self-induced noise, so-called ego-noise. While external ambient noise sources vary from environment to environment, ego-noise…
In a scenario with multiple persons talking simultaneously, the spatial characteristics of the signals are the most distinct feature for extracting the target signal. In this work, we develop a deep joint spatial-spectral non-linear filter that can be steered to an arbitrary target direction. For th…
In this paper, we present a causal speech signal improvement system that is designed to handle different types of distortions. The method is based on a generative diffusion model which has been shown to work well in scenarios with missing data and non-linear corruptions. To guarantee causal processi…
Single-channel deep speech enhancement approaches often estimate a single multiplicative mask to extract clean speech without a measure of its accuracy. Instead, in this work, we propose to quantify the uncertainty associated with clean speech estimates in neural network-based speech enhancement. Pr…
This work focuses on online dereverberation for hearing devices using the weighted prediction error (WPE) algorithm. WPE filtering requires an estimate of the target speech power spectral density (PSD). Recently deep neural networks (DNNs) have been used for this task. However, these approaches opti…
One of the most prominent challenges in the field of diffractive imaging is the phase retrieval (PR) problem: In order to reconstruct an object from its diffraction pattern, the inverse Fourier transform must be computed. This is only possible given the full complex-valued diffraction data, i.e. mag…
Speech enhancement in the time-frequency domain is often performed by estimating a multiplicative mask to extract clean speech. However, most neural network-based methods perform point estimation, i.e., their output consists of a single mask. In this paper, we study the benefits of modeling uncertai…
Recently, variational autoencoders have been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. However, variational autoencoders are trained on clean speech only, which results in a limited ability of extracting the speech signal…
Recent advances in the design of neural network architectures, in particular those specialized in modeling sequences, have provided significant improvements in speech separation performance. In this work, we propose to use a bio-inspired architecture called Fully Recurrent Convolutional Neural Netwo…
Recently, a generative variational autoencoder (VAE) has been proposed for speech enhancement to model speech statistics. However, this approach only uses clean speech in the training phase, making the estimation particularly sensitive to noise presence, especially in low signal-to-noise ratios (SNR…
In this work, we investigate if the learned encoder of the end-to-end convolutional time domain audio separation network (Conv-TasNet) is the key to its recent success, or if the encoder can just as well be replaced by a deterministic hand-crafted filterbank. Motivated by the resemblance of the trai…
A common processing pipeline for multichannel speech enhancement is to combine a linear spatial filter with a single-channel postfilter. In fact, it can be shown that such a combination is optimal in the minimum mean square error (MMSE) sense if the noise follows a multivariate Gaussian distribution…
Robust and accurate estimation of liquid height lies as an essential part of pouring tasks for service robots. However, vision-based methods often fail in occluded conditions while audio-based methods cannot work well in a noisy environment. We instead propose a multimodal pouring network (MP-Net) t…
In this work, the generalization of speech enhancement algorithms based on deep neural networks (DNNs) for training datasets that differ in size and diversity is analyzed. For this, we compare noise aware training (NAT) features and signal-to-noise ratio (SNR) based noise aware training (SNR-NAT) fe…
In this paper, we focus on the challenging perception problem in robotic pouring. Most of the existing approaches either leverage visual or haptic information. However, these techniques may suffer from poor generalization performances on opaque containers or concerning measuring precision. To tackle…
Most Bayesian clean speech estimators, like the Wiener filter or Ephraim and Malah's amplitude estimators, are derived under the assumption that the true power spectral density (PSD) of speech is known. In practice, however, only estimates are available. When the PSD estimation errors are neglected,…
We present in this paper two loss functions tailored for rare audio event detection in audio streams. The weighted loss is designed to tackle the common issue of imbalanced data in background/foreground classification while the multi-task loss enables the networks to simultaneously model the class d…
Due to the low computational complexity and the low memory consumption, first-order recursive smoothing is a technique often applied to estimate the mean of a random process. For instance, recursive smoothing is used in noise power estimators where adaptively changing smoothing factors are used inst…
Perceptual measures are usually considered more reliable than instrumental measures for evaluating the perceived level of reverberation. However, such measures are costly in both time and money, and, due to variations in stimuli or assessors, the resulting data is not always statistically significan…
For single-microphone noise reduction, a minimum variance distortionless response (MVDR) filter has been proposed recently. This filter takes the speech correlations of consecutive time frames into account and achieves impressive results in terms of speech distortions even in a blind implementation…
We propose sparse reconstruction techniques to improve the quality and/or reduce the bit-rate of standard speech coders. To that end, we assume signal sparsity in some transform domain and formulate the problem of reconstructing the original signal in terms of constrained ℓ <sub xmlns:mml="http://ww…
The robustness of speech recognizers towards noise can be increased by normalizing the statistical moments of the Mel-frequency cepstral coefficients (MFCCs), e. g. by using cepstral mean normalization (CMN) or cepstral mean and variance normalization (CMVN). The necessary statistics are estimated o…
In this paper we perform an extensive theoretical and experimental comparison of two recently proposed multi-channel speech dereverberation algorithms. Both of them are based on the multi-channel Wiener filter but they use different estimators of the speech and reverberation power spectral densities…
In many acoustic conditions the recorded speech signals may be severely affected by reverberation, leading to a reduced speech quality and intelligibility. In this paper we focus on a blind speech dereverberation method based on multi-channel linear prediction (MCLP) in the short-time Fourier transf…
For the enhancement of speech degraded by noise, accurate estimation of the noise power spectral density (PSD) is indispensable, especially if only a single microphone signal is available. Fast and accurate tracking of the noise PSD is particularly challenging in highly non-stationary noise types, s…