104 citations · 305 across the 50 of their papers we have counts for
12 papers · 1 filter
SUNAC: Source-aware Unified Neural Audio Codec
Ryo Aihara, Yoshiki Masuyama, Francesco Paissan +3
Neural audio codecs (NACs) provide compact representations that can be leveraged in many downstream applications, in particular large language models. Yet most NACs encode mixtures…
Local Density-Based Anomaly Score Normalization for Domain Generalization
Kevin Wilkinghoff, Haici Yang, Janek Ebbers +3
State-of-the-art anomalous sound detection (ASD) systems in domain-shifted conditions rely on projecting audio signals into an embedding space and using distance-based outlier dete…
FlexIO: Flexible Single- and Multi-Channel Speech Separation and Enhancement
Yoshiki Masuyama, Kohei Saijo, Francesco Paissan +6
Speech separation and enhancement (SSE) has advanced remarkably and achieved promising results in controlled settings, such as a fixed number of speakers and a fixed array configur…
Exploring Disentangled Neural Speech Codecs from Self-Supervised Representations
Ryo Aihara, Yoshiki Masuyama, Gordon Wichern +2
Neural audio codecs (NACs), which use neural networks to generate compact audio representations, have garnered interest for their applicability to many downstream tasks -- especial…
FasTUSS: Faster Task-Aware Unified Source Separation
Francesco Paissan, Gordon Wichern, Yoshiki Masuyama +4
Time-Frequency (TF) dual-path models are currently among the best performing audio source separation network architectures, achieving state-of-the-art performance in speech enhance…
Physics-Informed Direction-Aware Neural Acoustic Fields
Yoshiki Masuyama, François G. Germain, Gordon Wichern +2
This paper presents a physics-informed neural network (PINN) for modeling first-order Ambisonic (FOA) room impulse responses (RIRs). PINNs have demonstrated promising performance i…
Factorized RVQ-GAN For Disentangled Speech Tokenization
Sameer Khurana, Dominik Klement, Antoine Laurent +13
We propose Hierarchical Audio Codec (HAC), a unified neural speech codec that factorizes its bottleneck into three linguistic levels-acoustic, phonetic, and lexical-within a single…
HASRD: Hierarchical Acoustic and Semantic Representation Disentanglement
Amir Hussein, Sameer Khurana, Gordon Wichern +2
Effective speech representations for spoken language models must balance semantic relevance with acoustic fidelity for high-quality reconstruction. However, existing approaches str…
Direction-Aware Neural Acoustic Fields for Few-Shot Interpolation of Ambisonic Impulse Responses
Christopher Ick, Gordon Wichern, Yoshiki Masuyama +2
The characteristics of a sound field are intrinsically linked to the geometric and spatial properties of the environment surrounding a sound source and a listener. The physics of s…
Data Augmentation Using Neural Acoustic Fields With Retrieval-Augmented Pre-training
Christopher Ick, Gordon Wichern, Yoshiki Masuyama +2
This report details MERL's system for room impulse response (RIR) estimation submitted to the Generative Data Augmentation Workshop at ICASSP 2025 for Augmenting RIR Data (Task 1)…
Retrieval-Augmented Neural Field for HRTF Upsampling and Personalization
Yoshiki Masuyama, Gordon Wichern, François G. Germain +2
Head-related transfer functions (HRTFs) with dense spatial grids are desired for immersive binaural audio generation, but their recording is time-consuming. Although HRTF spatial u…
30+ Years of Source Separation Research: Achievements and Future Challenges
Shoko Araki, Nobutaka Ito, Reinhold Haeb-Umbach +3
Source separation (SS) of acoustic signals is a research field that emerged in the mid-1990s and has flourished ever since. On the occasion of ICASSP's 50th anniversary, we review…