Spatial Audio

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15 papers in the last four weeks, up 275% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 54

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Learning-based Physics-Constrained Neural Kernel for Sound Field Estimation With Source-Position-Dependent Directional Weighting
Jul 7, 2026Mattia Marella, Shoichi KoyamaKernel Regression Implicit Neural Representations
National Institute of Informatics, Tokyo, Japan · University of Ferrara, Ferrara, Italy
Weakly Guided and Autoregressive Beamformer Parameterization for Generalizable Moving Speaker Extraction in Higher-Order Ambisonics
Jul 5, 2026Jakob Kienegger, Tal Peer, Sina Khanagha +1Speech Enhancement Spatial Audio
Signal Processing (SP) Group, University of Hamburg
SelectTSL: Prompt-Guided Selective Target Sound Localization in Complex Scenarios
Jul 2, 2026Ziyang Jiang, Yu Chen, Zexu Pan +5Direction-of-Arrival Estimation Spatial Audio
Ziyang Jiang, Student Member, IEEE, Yu Chen, Zexu Pan, Member, IEEE, Xinyuan Qian, Senior Member, IEEE, Bowen Xing, Ivor W. Tsang, Fellow, IEEE, Xu-Cheng Yin, Senior Member, IEEE, Haizhou Li, Fellow, IEEE
Speaker head orientation estimation with a single microphone array using phase spectrogram features
Jul 2, 2026Balint Turi, Archontis Politis, Parthasaarathy Sudarsanam +1Convolutional RNNs Spatial Audio
Audio Research Group, Tampere University, Finland
Position-Aware Target Speaker Extraction for Long-Form Multi-Party Conversations: A Diarization-Free Framework for ASR
Jun 28, 2026Yichi Wang, Junzhe Chen, Wangjin Zhou +1Target Speaker Extraction Speech Processing
Graduate School of Informatics, Kyoto University, Kyoto, Japan
From General-Purpose Audio Tagging to Spatially Grounded Sound Event Localization and Detection
Jun 26, 2026Stefano Giacomelli, Stefano Damiano, Claudia Rinaldi +2Audio Representation Learning Direction-of-Arrival Estimation
DISIM Department, University of L’Aquila, Italy · ESAT Department, KU Leuven, Belgium · CNIT, Research Unit of L’Aquila, Italy +1
Statistical validation and full-sphere extension of a Bayesian model for human static sound localisation
Jun 23, 2026Roberto Barumerli, Fabian Brinkmann, Emanuele Zanoni +3Bayesian Inference Spatial Audio
Dyson School of Design Engineering, Imperial College London, London, United Kingdom · Audio Communication Group, Technische Universität Berlin, Germany · Department of Industrial Systems Technology and Management, University of Padova, Vicenza, Italy
Evaluation of Headrest-Integrated Loudspeakers for Enhanced Spatial Audio Immersion in Automotive Cabins
Jun 23, 2026Martin Wolters, Jacobo Giralt, Harald Mundt +1Spatial Audio
Dolby Germany GmbH, Nürnberg, Germany
Generalised Transcoding Framework for Arbitrary Spatial Audio Capture and Playback Formats
Jun 16, 2026Archontis Politis, Janani Fernandez, Leo McCormackSpatial Audio
Faculty of Information Technology and Communication Sciences, Tampere University, Finland · Department of Information and Communications Engineering, Aalto University, Espoo, Finland
Spectro-Temporal Interference Confounds Phase Encoding in Spatial Audio Foundation Models
Jun 12, 2026Yuxuan Chen, Haoyuan Yu, Peize HeAudio Representation Learning Direction-of-Arrival Estimation
The Chinese University of Hong Kong, Shenzhen · Jilin University · Hunan University +1
Spatio-Temporal Audio Language Modeling for Dynamic Sound Sources
Jun 12, 2026Oh Hyun-Bin, Kazuki Shimada, Yuhta Takida +6Audio-Language Model Evaluation Audio Representation Learning
1POSTECH · 2Sony AI · 4Sungkyunkwan University +2
Sensitivity Analysis of Generative Spatial Audio Metrics: A Study on Responsiveness, Smoothness, and Symmetry
Jun 10, 2026Purnima Kamath, Adrian S. Roman, Koichi Saito +2Automated Evaluation Spatial Audio
New York University, New York, USA · Sony AI, New York, USA · Sony Group Corporation, Tokyo, Japan
Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding
Jun 9, 2026Zhiyuan Zhu, Yixuan Chen, Yiwen Shao +13Multimodal Large Language Models Audio Understanding
Zhejiang University · Tencent Hunyuan
BiEAR: A Human Auditory-Inspired Adaptive Binaural Front-end for Multi-Speaker Localisation and Distance Estimation
Jun 5, 2026Hanyu Meng, Eliathamby Ambikairajah, Vidhyasaharan Sethu +2Adaptive Inference Direction-of-Arrival Estimation
The University of New South Wales, Sydney, Australia · Tongyi Speech Lab, Alibaba Group, Hangzhou, China · School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen, China
Probing Spatial Structure in Pretrained Audio Representations
Jun 4, 2026Chuyang Chen, Sivan Ding, Adrian S. Roman +1Audio Representation Learning Representation Probing
Music and Audio Research Laboratory, New York University, USA
SHB-AE: Spherical harmonic beamforming based Ambisonics encoding and upscaling method for smartphone microphone array
Jun 3, 2026Yuhuan You, Yufan Qian, Tianshu Qu +2Spherical Harmonics Spatial Audio
Peking University · Beijing Xiaomi Mobile Software Co., Ltd · Xiaomi Communications Co., Ltd
Flow-HOA: Generative Joint Optimization for Ambisonics Encoding via Flow Matching
Jun 3, 2026Yuhuan You, Yufan Qian, Tianshu Qu +2Spatial Audio
State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University, Beijing, China · 2Beijing Xiaomi Mobile Software Co., Ltd · 3Xiaomi Communications Co., Ltd
Audio Spotforming via Post-Filtering Using Cross-Array Non-target Estimates
Jun 2, 2026Yuto Ishikawa, Li Li, Shogo Seki +1Target Speaker Extraction Speech Enhancement
CyberAgent, Inc., Japan · The University of Tokyo, Japan
Towards Streaming Synchronized Spatial Audio Generation via Autoregressive Diffusion Transformer
May 29, 2026Ke Lei, Yu Zhang, Changhao Pan +4Text-to-Audio Generation Audio-Video Generation
1Zhejiang University, China · 2ByteDance
3DAE: Binaural Quality Assessment for Audio Novel View Synthesis with Spatial Maps and Benchmark
May 28, 2026Jialu Xu, Yifan ZhouSpatial Audio Audio Generation
University of Waterloo
EigeNet: Geometry-Informed Multi-Modal Learning for Few-shot Novel View RIR Prediction
May 27, 2026Chong Jing, Zitong Lan, Junan Zhang +1Multi-View Learning Few-Shot Learning
The Chinese University of Hong Kong, Shenzhen · University of Pennsylvania
IsoNet: Spatially-aware audio-visual target speech extraction in complex acoustic environments
May 14, 2026Dinanath Padhya, Sajen Maharjan, Binita Adhikari +1Audio-Visual Understanding Target Speaker Extraction
Department of Electronics and Computer Engineering, Thapathali Campus, Institute of Engineering, Tribhuvan University, Kathmandu, Nepal
Autoregressive Guidance of Deep Spatially Selective Filters using Bayesian Tracking for Efficient Extraction of Moving Speakers
Mar 24, 2026Jakob Kienegger, Timo GerkmannBayesian Filtering Direction-of-Arrival Estimation
Signal Processing Group, Department of Informatics, University of Hamburg, 22527 Hamburg, Germany
DiffAU: Diffusion-Based Ambisonics Upscaling
Sep 30, 2025Amit Milstein, Nir Shlezinger, Boaz RafaelyAudio Diffusion Models Spatial Audio
ECE School Ben-Gurion University of the Negev, Be’er-Sheva, Israel

Jul 7, 2026, cs.SDJ/K move · Enter open · S save

Learning-based Physics-Constrained Neural Kernel for Sound Field Estimation With Source-Position-Dependent Directional Weighting

Mattia Marella, Shoichi Koyama

National Institute of Informatics, Tokyo, Japan · University of Ferrara, Ferrara, Italy

Kernel Regression Implicit Neural Representations Kernel Methods Physics-Informed ML Spatial Audio

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A learning-based physics-constrained neural kernel for sound field estimation is proposed. Sound field estimation aims to estimate the spatial distribution of an acoustic field from a discrete set of microphone measurements, which have a wide range of applications. Among existing sound field estimation methods, kernel-regression-based methods offer a flexible and principled framework for incorporating physical constraints and allow inference through linear operation. It is also possible to adapt the kernel function to the target acoustic environment by representing the directional weighting function as an implicit neural representation (INR) and optimizing hyperparameters using measurements. However, the kernel function is generally optimized for single snapshot measurements of the microphones, which can lead to strong overfitting and poor generalization. We propose a source-position-dependent INR for the directional weighting function, enabling the kernel function to capture common directional patterns and to generalize to unseen source positions in the target acoustic environment. Experimental results indicate that our proposed method outperforms the snapshot-based method by estimating a directional weighting function that matches the directivity of the target sound field.

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