328 citations · 883 across the 52 of their papers we have counts for
54 papers
Quantifying the Uncertainty of Blindly Estimated Room Embeddings Using a Dispersion-Calibrated Score
Yang Xiang, Philipp Götz, Emanuël A. P. Habets +3
Room embeddings derived from reverberant speech are often unreliable: speech content and recording degradation can alter the representation even when speaker, room, and source-rece…
Matching Reverberant Speech Through Learned Acoustic Embeddings and Feedback Delay Networks
Philipp Götz, Gloria Dal Santo, Sebastian J. Schlecht +2
Reverberation conveys critical acoustic cues about the environment, supporting spatial awareness and immersion. For auditory augmented reality (AAR) systems, generating perceptuall…
Neural Directional Filtering with Configurable Directivity Pattern at Inference
Weilong Huang, Srikanth Raj Chetupalli, Emanuël A. P. Habets
Spatial filtering with a desired directivity pattern is advantageous for many audio applications. In this work, we propose neural directional filtering with user-defined directivit…
Stereo Reproduction in the Presence of Sample Rate Offsets
Srikanth Korse, Andreas Walther, Emanuel A. P. Habets
One of the main challenges in synchronizing wirelessly connected loudspeakers for spatial audio reproduction is clock skew. Clock skew arises from sample rate offsets ( SROs) betwe…
Training Strategies for Modality Dropout Resilient Multi-Modal Target Speaker Extraction
Srikanth Korse, Mohamed Elminshawi, Emanuel A. P. Habets +1
The primary goal of multi-modal TSE (MTSE) is to extract a target speaker from a speech mixture using complementary information from different modalities, such as audio enrolment a…
Dynamic Slimmable Networks for Efficient Speech Separation
Mohamed Elminshawi, Srikanth Raj Chetupalli, Emanuël A. P. Habets
Recent progress in speech separation has been largely driven by advances in deep neural networks, yet their high computational and memory requirements hinder deployment on resource…
Sample Rate Offset Compensated Acoustic Echo Cancellation For Multi-Device Scenarios
Srikanth Korse, Oliver Thiergart, Emanuel A. P. Habets
Acoustic echo cancellation (AEC) in multi-device scenarios is a challenging problem due to sample rate offset (SRO) between devices. The SRO hinders the convergence of the AEC filt…
Low-Complexity Neural Wind Noise Reduction for Audio Recordings
Hesam Eftekhari, Srikanth Raj Chetupalli, Shrishti Saha Shetu +2
Wind noise significantly degrades the quality of outdoor audio recordings, yet remains difficult to suppress in real-time on resource-constrained devices. In this work, we propose…
VoxATtack: A Multimodal Attack on Voice Anonymization Systems
Ahmad Aloradi, Ünal Ege Gaznepoglu, Emanuël A. P. Habets +1
Voice anonymization systems aim to protect speaker privacy by obscuring vocal traits while preserving the linguistic content relevant for downstream applications. However, because…
Benchmarking Neural Speech Codec Intelligibility with SITool
Anna Leschanowsky, Kishor Kayyar Lakshminarayana, Anjana Rajasekhar +4
Speech intelligibility assessment is essential for evaluating neural speech codecs, yet most evaluation efforts focus on overall quality rather than intelligibility. Only a few pub…
You Are What You Say: Exploiting Linguistic Content for VoicePrivacy Attacks
Ünal Ege Gaznepoglu, Anna Leschanowsky, Ahmad Aloradi +4
Speaker anonymization systems hide the identity of speakers while preserving other information such as linguistic content and emotions. To evaluate their privacy benefits, attacks…
On the Relation Between Speech Quality and Quantized Latent Representations of Neural Codecs
Mhd Modar Halimeh, Matteo Torcoli, Philipp Grundhuber +1
Neural audio signal codecs have attracted significant attention in recent years. In essence, the impressive low bitrate achieved by such encoders is enabled by learning an abstract…
Transparent NLP: Using RAG and LLM Alignment for Privacy Q&A
Anna Leschanowsky, Zahra Kolagar, Erion Çano +4
The transparency principle of the General Data Protection Regulation (GDPR) requires data processing information to be clear, precise, and accessible. While language models show pr…
Low-Resource Text-to-Speech Synthesis Using Noise-Augmented Training of ForwardTacotron
Kishor Kayyar Lakshminarayana, Frank Zalkow, Christian Dittmar +2
In recent years, several text-to-speech systems have been proposed to synthesize natural speech in zero-shot, few-shot, and low-resource scenarios. However, these methods typically…
GAN-Based Speech Enhancement for Low SNR Using Latent Feature Conditioning
Shrishti Saha Shetu, Emanuël A. P. Habets, Andreas Brendel
Enhancing speech quality under adverse SNR conditions remains a significant challenge for discriminative deep neural network (DNN)-based approaches. In this work, we propose DisCoG…
Align-ULCNet: Towards Low-Complexity and Robust Acoustic Echo and Noise Reduction
Shrishti Saha Shetu, Naveen Kumar Desiraju, Wolfgang Mack +1
The successful deployment of deep learning-based acoustic echo and noise reduction (AENR) methods in consumer devices has spurred interest in developing low-complexity solutions, w…
Neural Directional Filtering: Far-Field Directivity Control With a Small Microphone Array
Julian Wechsler, Srikanth Raj Chetupalli, Mhd Modar Halimeh +2
Capturing audio signals with specific directivity patterns is essential in speech communication. This study presents a deep neural network (DNN)-based approach to directional filte…
A Hybrid Approach for Low-Complexity Joint Acoustic Echo and Noise Reduction
Shrishti Saha Shetu, Naveen Kumar Desiraju, Jose Miguel Martinez Aponte +2
Deep learning-based methods that jointly perform the task of acoustic echo and noise reduction (AENR) often require high memory and computational resources, making them unsuitable…
Comparative Analysis Of Discriminative Deep Learning-Based Noise Reduction Methods In Low SNR Scenarios
Shrishti Saha Shetu, Emanuël A. P. Habets, Andreas Brendel
In this study, we conduct a comparative analysis of deep learning-based noise reduction methods in low signal-to-noise ratio (SNR) scenarios. Our investigation primarily focuses on…
ConcateNet: Dialogue Separation Using Local And Global Feature Concatenation
Mhd Modar Halimeh, Matteo Torcoli, Emanuël Habets
Dialogue separation involves isolating a dialogue signal from a mixture, such as a movie or a TV program. This can be a necessary step to enable dialogue enhancement for broadcast-…
Blind Acoustic Parameter Estimation Through Task-Agnostic Embeddings Using Latent Approximations
Philipp Götz, Cagdas Tuna, Andreas Brendel +2
We present a method for blind acoustic parameter estimation from single-channel reverberant speech. The method is structured into three stages. In the first stage, a variational au…
Meta Learning Text-to-Speech Synthesis in over 7000 Languages
Florian Lux, Sarina Meyer, Lyonel Behringer +5
In this work, we take on the challenging task of building a single text-to-speech synthesis system that is capable of generating speech in over 7000 languages, many of which lack s…
Speech Loudness in Broadcasting and Streaming
Matteo Torcoli, Mhd Modar Halimeh, Thomas Leitz +6
The introduction and regulation of loudness in broadcasting and streaming brought clear benefits to the audience, e.g., a level of uniformity across programs and channels. Yet, spe…
Data-driven Joint Detection and Localization of Acoustic Reflectors
H. Nazim Bicer, Cagdas Tuna, Andreas Walther +1
Room geometry inference algorithms rely on the localization of acoustic reflectors to identify boundary surfaces of an enclosure. Rooms with highly absorptive walls or walls at lar…