25 accepted papers
The ICASSP 2026 URGENT Challenge advances the series by focusing on universal speech enhancement (SE) systems that handle diverse distortions, domains, and input conditions. This overview paper details the challenge's motivation, task definitions, datasets, baseline systems, evaluation protocols, an…
Large language models (LLMs) demand extensive memory capacity during both fine-tuning and inference. To enable memory-efficient fine-tuning, existing methods apply block-wise quantization techniques, such as NF4 and AF4, to the network weights. We show that these quantization techniques incur subopt…
"Distributed computing in the context of deep neural networks (DNNs) implies the execution of one part of the network on edge devices and the other part typically on a large-scale cloud platform. Conventional methods propose to employ a serial concatenation of a learned image and source encoder, the…
In recent years, the introduction of neural networks (NNs) into the field of speech enhancement has brought significant improvements. However, many of the proposed methods are quite demanding in terms of computational complexity and memory footprint. For the application in dedicated communication de…
Most deep noise suppression (DNS) models are trained with reference-based losses requiring access to clean speech. However, sometimes an additive microphone model is insufficient for real-world applications. Accordingly, ways to use real training data in supervised learning for DNS models promise to…
Deep neural network (DNN)-based approaches to acoustic echo cancellation (AEC) and hybrid speech enhancement systems have gained increasing attention recently, introducing significant performance improvements to this research field. Using the fully convolutional recurrent network (FCRN) architecture…
While deep neural networks (DNNs) achieve impressive performance on environment perception tasks, their sensitivity to adversarial perturbations limits their use in practical applications. In this paper, we (i) propose a novel adversarial perturbation detection scheme based on multi-task perception…
Rare sound event detection (rare SED) deals with obtaining valuable information from data consisting mostly of acoustic background noises. It has meanwhile a long research history and was part of the DCASE 2017 Challenge. State-of-the-art performance is currently reached using a stacked combination…
Acoustic echo cancellation (AEC) algorithms have a long-term steady role in signal processing, with approaches improving the performance of applications such as automotive hands-free systems, smart home and loudspeaker devices, or web conference systems. Just recently, very first deep neural network…
Recording a meeting and obtaining clean speech signals of each speaker is a challenging task. Even with a multichannel recording, in which all speakers are equipped with a close-talk microphone, speech of an active speaker still couples not only into his dedicated microphone, but also into all other…
There are many ways to evaluate rare sound event detection (SED) approaches, e.g., the DCASE 2017 challenge provides a widely employed framework. This paper proposes a rare SED training and test framework, which is reflecting an SED application in a more realistic way. Our setup gets rid of too much…
Convolutional recurrent neural networks (CRNs) using convolutional encoder-decoder (CED) structures have shown promising performance for single-channel speech enhancement. These CRNs handle temporal modeling through integrating long short-term memory (LSTM) layers in between convolutional encoder an…
Self-supervised monocular depth estimation presents a powerful method to obtain 3D scene information from single camera images, which is trainable on arbitrary image sequences without requiring depth labels, e.g., from a LiDAR sensor. In this work we present a new self-supervised semantically-guided…
Estimating time-frequency domain masks for speech enhancement using deep learning approaches has recently become a popular field in research. In this paper, we propose a novel components loss (CL) for the training of neural networks for speech enhancement. During the training process, the proposed C…
Acoustic feedback cancellation has gained a major and steady role in the research fields of signal processing over the past decades, since it is inevitable for numerous applications such as hearing aids or in-car communication systems. In this paper, we investigate measurement noise covariance estim…
We introduce a method to improve the quality of simple scalar quantization in the context of acoustic sensor networks by combining ideas from sparse reconstruction, artificial neural networks and weighting filters. We start from the observation that optimization methods based on sparse reconstructio…
A priori signal-to-noise ratio (SNR) contains critical information about the single-channel mixture of a speech and noise signal, and can be used by speech enhancement algorithms. In this paper, we propose a novel a priori SNR estimator using the estimates obtained from discriminative non-negative m…
In this work, we present a simple deep neural network (DNN)-based regression approach to artificial speech bandwidth extension (ABE) in the frequency domain for estimating missing speech components in the range 4 ... 7 kHz. The upper band (UB) spectral magnitudes are found by first estimating the UB…
Emerging use cases, such as keyword spotting while listening to FM radio, or participating in a teleconference utilizing the hands-free system in a vehicle, require the utilization of multi-channel acoustic echo cancellation (AEC). Addressing the typically remaining residual echo, it is common pract…
Multichannel recordings of meetings with a (wireless) headset for each person deliver commonly the best audio quality for subsequent analyses. However, still speech portions of other participants can couple into the microphone channel of the associated target speaker. Due to this crosstalk, a speake…
In studies on artificial bandwidth extension (ABE), there is a lack of international coordination in subjective tests between multiple methods and languages. Here we present the design of absolute category rating listening tests evaluating 12 ABE variants of six approaches in multiple languages, nam…
Choosing among competing models of collected data is crucial for all sciences. In the last decade there has been an increasing tendency to use Bayesian methods throughout many fields. When assessing the performance of instrumental measures of speech quality, classical measures such as correlation co…
While most pattern recognition approaches are designed and trained with clearly defined reference labels, there are a few new applications working with ambiguous ones. Since the linear discriminant analysis (LDA) is one of the most utilized methods in pattern recognition to reduce the dimensionality…
The ITU-T Recommendation G.722 about subband adaptive differential pulse code modulation (SB-ADPCM) is the mandatory wideband speech codec in the new generation digital enhanced cordless telephony (NG-DECT). Although in ADPCM the difference signal instead of the original signal is quantized and adap…
This contribution presents a robust approach to acoustic event source localization for surveillance under reverberant environmental conditions. In particular, we support the classical generalized cross-correlation algorithm with phase transform weighting (GCC-PHAT) and the steered response power (SR…