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相关论文: Single-Microphone-Based Sound Source Localization …

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This paper addresses the problem of multiple-speaker localization in noisy and reverberant environments, using binaural recordings of an acoustic scene. A Gaussian mixture model (GMM) is adopted, whose components correspond to all the…

声音 · 计算机科学 2017-10-06 Xiaofei Li , Laurent Girin , Sharon Gannot , Radu Horaud

Purpose: Surgical scene understanding is key to advancing computer-aided and intelligent surgical systems. Current approaches predominantly rely on visual data or end-to-end learning, which limits fine-grained contextual modeling. This work…

This paper introduces an area-based source separation method designed for virtual meeting scenarios. The aim is to preserve speech signals from an unspecified number of sources within a defined spatial area in front of a linear microphone…

音频与语音处理 · 电气工程与系统科学 2024-08-20 Martin Strauss , Okan Köpüklü

Acoustic source localization has been applied in different fields, such as aeronautics and ocean science, generally using multiple microphones array data to reconstruct the source location. However, the model-based beamforming methods fail…

声音 · 计算机科学 2022-04-01 Guanxing Zhou , Hao Liang , Xinghao Ding , Yue Huang , Xiaotong Tu , Saqlain Abbas

This paper introduces a new framework for supervised sound source localization referred to as virtually-supervised learning. An acoustic shoe-box room simulator is used to generate a large number of binaural single-source audio scenes.…

声音 · 计算机科学 2017-03-21 Saurabh Kataria , Clément Gaultier , Antoine Deleforge

Speech separation with several speakers is a challenging task because of the non-stationarity of the speech and the strong signal similarity between interferent sources. Current state-of-the-art solutions can separate well the different…

信号处理 · 电气工程与系统科学 2021-02-09 Nicolas Furnon , Romain Serizel , Irina Illina , Slim Essid

Sound event localization aims at estimating the positions of sound sources in the environment with respect to an acoustic receiver (e.g. a microphone array). Recent advances in this domain most prominently focused on utilizing deep…

Self-supervised learning has been used to leverage unlabelled data, improving accuracy and generalisation of speech systems through the training of representation models. While many recent works have sought to produce effective…

计算与语言 · 计算机科学 2023-10-18 Antoni Dimitriadis , Siqi Pan , Vidhyasaharan Sethu , Beena Ahmed

This paper describes a system that gives a mobile robot the ability to perform automatic speech recognition with simultaneous speakers. A microphone array is used along with a real-time implementation of Geometric Source Separation and a…

Acoustic scene perception involves describing the type of sounds, their timing, their direction and distance, as well as their loudness and reverberation. While audio language models excel in sound recognition, single-channel input…

声音 · 计算机科学 2025-10-08 Xilin Jiang , Hannes Gamper , Sebastian Braun

The prevailing noise-resistant and reverberation-resistant localization algorithms primarily emphasize separating and providing directional output for each speaker in multi-speaker scenarios, without association with the identity of…

声音 · 计算机科学 2023-10-18 Yu Chen , Xinyuan Qian , Zexu Pan , Kainan Chen , Haizhou Li

A deep learning approach based on big data is proposed to locate broadband acoustic sources using a single hydrophone in ocean waveguides with uncertain bottom parameters. Several 50-layer residual neural networks, trained on a huge number…

大气与海洋物理 · 物理学 2019-07-19 Haiqiang Niu , Zaixiao Gong , Emma Ozanich , Peter Gerstoft , Haibin Wang , Zhenglin Li

We study two cases of acoustic source localization in a reverberant room, from a number of point-wise narrowband measurements. In the first case, the room is perfectly known. We show that using a sparse recovery algorithm with a dictionary…

信息论 · 计算机科学 2013-07-19 Gilles Chardon , Laurent Daudet

Visual events are usually accompanied by sounds in our daily lives. We pose the question: Can the machine learn the correspondence between visual scene and the sound, and localize the sound source only by observing sound and visual scene…

计算机视觉与模式识别 · 计算机科学 2019-02-18 Arda Senocak , Tae-Hyun Oh , Junsik Kim , Ming-Hsuan Yang , In So Kweon

Signal source localization has been a problem of interest in the multi-robot systems domain given its applications in search & rescue and hazard localization in various industrial and outdoor settings. A variety of multi-robot search…

机器人学 · 计算机科学 2025-07-14 Aditya Bhatt , Mary Katherine Corra , Franklin Merlo , Prajit KrisshnaKumar , Souma Chowdhury

Despite the overwhelming success of deep learning in various speech processing tasks, the problem of separating simultaneous speakers in a mixture remains challenging. Two major difficulties in such systems are the arbitrary source…

声音 · 计算机科学 2017-11-30 Zhuo Chen , Yi Luo , Nima Mesgarani

In service robotics, there is an interest to identify the user by voice alone. However, in application scenarios where a service robot acts as a waiter or a store clerk, new users are expected to enter the environment frequently. Typically,…

音频与语音处理 · 电气工程与系统科学 2018-09-13 Ivette Vélez , Caleb Rascon , Gibrán Fuentes-Pineda

The advance of technology for transmitting Data-over-Sound in various IoT and telecommunication applications has led to the concept of machine-to-machine over-the-air acoustic signalling. Reverberation can have a detrimental effect on such…

音频与语音处理 · 电气工程与系统科学 2019-08-14 Amogh Matt , Dan Stowell

Source seeking is an important topic in robotic research, especially considering sound-based sensors since they allow the agents to locate a target even in critical conditions where it is not possible to establish a direct line of sight. In…

Separation of simultaneously active multiple speakers is a difficult task in environments with strong reverberation and many background noise sources. This paper uses the relative transfer matrix (ReTM), a generalization of the relative…

音频与语音处理 · 电气工程与系统科学 2025-03-13 Wageesha N. Manamperi , Thushara D. Abhayapala