104 citations · 305 across the 50 of their papers we have counts for
60 papers
Evaluating Inference-time Algorithms for Semantic Sound Scene Segmentation
Sripathi Sridhar, Gordon Wichern, Yoshiki Masuyama +2
Spatial semantic segmentation of sound scenes (S5) involves the detection and extraction of target sound events in audio files. However, in typical detection-extraction pipelines,…
Transcript-Supervised Post-Training of Generative Speech Enhancement on Real Recordings via Reinforce Adjoint Matching
Julius Richter, Christoph Boeddeker, Yoshiki Masuyama +4
We adapt Reinforce Adjoint Matching (RAM), a reward-based post-training method, to generative speech enhancement (SE). Starting from a pretrained SE model, RAM tilts the model's co…
Downstream-Task-Aware Unified Source Separation
Yoshiki Mitsui, Ryo Aihara, Tatsuhiko Saito +5
Task-aware unified source separation (TUSS) enables a single model to handle diverse separation tasks by conditioning on input prompts. However, conventional TUSS does not account…
Anomalous Sound Detection Meets Noise-Aware Self-Supervised Learning
Takuya Fujimura, Gordon Wichern, Yoshiki Masuyama +5
In this paper, we introduce noise-aware self-supervised learning (NA-SSL) models for noise-aware anomalous sound detection (NA-ASD). NA-ASD is an ASD task with two-channel audio re…
NABEATs: Noise-Aware Audio Representation Learning
Takuya Fujimura, Yoshiki Masuyama, Gordon Wichern +3
We propose the concept of noise-aware audio self-supervised learning (SSL), whose goal is to encode audio mixtures while suppressing undesired noise, and present Noise-Aware BEATs…
Technical Report for MERL's Real-TSE Challenge Submission
Dominik Klement, Yoshiki Masuyama, Christoph Boeddeker +4
Target speech extraction (TSE) has largely been dominated by neural network-based approaches trained and evaluated on synthetic fully overlapped data. The Real-TSE Challenge aims t…
Predictive-Generative Drift Decomposition for Speech Enhancement and Separation
Julius Richter, Yoshiki Masuyama, Christoph Boeddeker +3
We propose a plug-and-play framework for speech enhancement and separation that augments predictive methods with a generative speech prior. Our approach, termed Stochastic Interpol…
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…
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…
Enhanced Reverberation as Supervision for Unsupervised Speech Separation
Kohei Saijo, Gordon Wichern, François G. Germain +2
Reverberation as supervision (RAS) is a framework that allows for training monaural speech separation models from multi-channel mixtures in an unsupervised manner. In RAS, models a…
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…