3 citations · 6 across the 19 of their papers we have counts for
21 papers
Velocity Potential Neural Field for Efficient Ambisonics Impulse Response Modeling
Yoshiki Masuyama, Francois G. Germain, Gordon Wichern +2
First-order Ambisonics (FOA) is a standard spatial audio format based on spherical harmonic decomposition. Its zeroth- and first-order components capture the sound pressure and par…
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…
Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models
Young Jin Park, Francois Germain, Jing Liu +6
Decision-making in building energy systems critically depends on the predictive accuracy of relevant time-series models. In scenarios lacking extensive data from a target building,…
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…
Task-Aware Unified Source Separation
Kohei Saijo, Janek Ebbers, François G. Germain +2
Several attempts have been made to handle multiple source separation tasks such as speech enhancement, speech separation, sound event separation, music source separation (MSS), or…
Leveraging Audio-Only Data for Text-Queried Target Sound Extraction
Kohei Saijo, Janek Ebbers, François G. Germain +3
The goal of text-queried target sound extraction (TSE) is to extract from a mixture a sound source specified with a natural-language caption. While it is preferable to have access…
TF-Locoformer: Transformer with Local Modeling by Convolution for Speech Separation and Enhancement
Kohei Saijo, Gordon Wichern, François G. Germain +2
Time-frequency (TF) domain dual-path models achieve high-fidelity speech separation. While some previous state-of-the-art (SoTA) models rely on RNNs, this reliance means they lack…
Sound Event Bounding Boxes
Janek Ebbers, Francois G. Germain, Gordon Wichern +1
Sound event detection is the task of recognizing sounds and determining their extent (onset/offset times) within an audio clip. Existing systems commonly predict sound presence con…
SMITIN: Self-Monitored Inference-Time INtervention for Generative Music Transformers
Junghyun Koo, Gordon Wichern, Francois G. Germain +2
We introduce Self-Monitored Inference-Time INtervention (SMITIN), an approach for controlling an autoregressive generative music transformer using classifier probes. These simple l…
Why does music source separation benefit from cacophony?
Chang-Bin Jeon, Gordon Wichern, François G. Germain +1
In music source separation, a standard training data augmentation procedure is to create new training samples by randomly combining instrument stems from different songs. These ran…
NIIRF: Neural IIR Filter Field for HRTF Upsampling and Personalization
Yoshiki Masuyama, Gordon Wichern, François G. Germain +4
Head-related transfer functions (HRTFs) are important for immersive audio, and their spatial interpolation has been studied to upsample finite measurements. Recently, neural fields…
NeuroHeed+: Improving Neuro-steered Speaker Extraction with Joint Auditory Attention Detection
Zexu Pan, Gordon Wichern, Francois G. Germain +2
Neuro-steered speaker extraction aims to extract the listener's brain-attended speech signal from a multi-talker speech signal, in which the attention is derived from the cortical…
Scenario-Aware Audio-Visual TF-GridNet for Target Speech Extraction
Zexu Pan, Gordon Wichern, Yoshiki Masuyama +4
Target speech extraction aims to extract, based on a given conditioning cue, a target speech signal that is corrupted by interfering sources, such as noise or competing speakers. B…
Generation or Replication: Auscultating Audio Latent Diffusion Models
Dimitrios Bralios, Gordon Wichern, François G. Germain +4
The introduction of audio latent diffusion models possessing the ability to generate realistic sound clips on demand from a text description has the potential to revolutionize how…
Late Audio-Visual Fusion for In-The-Wild Speaker Diarization
Zexu Pan, Gordon Wichern, François G. Germain +2
Speaker diarization is well studied for constrained audios but little explored for challenging in-the-wild videos, which have more speakers, shorter utterances, and inconsistent on…