Related papers: Speaker localization using direct path dominance t…
A robust method for linear array is proposed to address the difficulty of direction-of-arrival (DOA) estimation in reverberant and noisy environments. A direct-path dominance test based on the onset detection is utilized to extract…
Speaker localization for binaural microphone arrays has been widely studied for applications such as speech communication, video conferencing, and robot audition. Many methods developed for this task, including the direct path dominance…
It has been noted that the identification of the time-frequency bins dominated by the contribution from the direct propagation of the target speaker can significantly improve the robustness of the direction-of-arrival estimation. However,…
Direction-of-arrival estimation of multiple speakers in a room is an important task for a wide range of applications. In particular, challenging environments with moving speakers, reverberation and noise, lead to significant performance…
In multi-speaker environments the direction of arrival (DOA) of a target speaker is key for improving speech clarity and extracting target speaker's voice. However, traditional DOA estimation methods often struggle in the presence of noise,…
Recently, a method has been proposed to estimate the direction of arrival (DOA) of a single speaker by minimizing the frequency-averaged Hermitian angle between an estimated relative transfer function (RTF) vector and a database of…
Estimation of the direction-of-arrival (DOA) of sound sources is an important step in sound field analysis. Rigid spherical microphone arrays allow the calculation of a compact spherical harmonic representation of the sound field. A basic…
In hearing aid applications, an important objective is to accurately estimate the direction of arrival (DOA) of multiple speakers in noisy and reverberant environments. Recently, we proposed a binaural DOA estimation method, where the DOAs…
In this paper, we propose a deep learning based multi-speaker direction of arrival (DOA) estimation with audio and visual signals by using permutation-free loss function. We first collect a data set for multi-modal sound source localization…
Accurate Direction-of-Arrival (DOA) estimation in reverberant environments remains a fundamental challenge for spatial audio applications. While deep learning methods have shown strong performance in such conditions, they typically lack a…
Spatial analysis of room acoustics is an ongoing research topic. Microphone arrays have been employed for spatial analyses with an important objective being the estimation of the direction-of-arrival (DOA) of direct sound and early room…
This paper presents a method for real-time estimation of 2-dimensional direction of arrival (2D-DOA) of one or more sound sources using a nonlinear array of three microphones. 2D-DOA is estimated employing frame-level time difference of…
The estimation of the time- and frequency-dependent coherent-to-diffuse power ratio (CDR) from the measured spatial coherence between two omnidirectional microphones is investigated. Known CDR estimators are formulated in a common…
This paper summarizes the methods used to localize the sources recorded for the LOCalization And TrAcking (LOCATA) challenge. The tasks of stationary sources and arrays were considered, i.e., tasks 1 and 2 of the challenge, which were…
Supervised learning based methods for source localization, being data driven, can be adapted to different acoustic conditions via training and have been shown to be robust to adverse acoustic environments. In this paper, a convolutional…
Accurate sound field reproduction in rooms is often limited by the lack of knowledge of the room characteristics. Information about the room shape or nearby reflecting boundaries can, in principle, be used to improve the accuracy of the…
Aiming at estimating the direction of arrival (DOA) of a desired speaker in a multi-talker environment using a microphone array, in this paper we propose a signal-informed method exploiting the availability of an external microphone…
We propose a direction-of-arrival (DOA) estimation method for Sound Event Localization and Detection (SELD). Direct estimation of DOA using a deep neural network (DNN), i.e. completely-datadriven approach, achieves high accuracy. However,…
We address the problem of estimating direction-of-arrivals (DOAs) for multiple acoustic sources in a reverberant environment using a spherical microphone array. It is well-known that multi-source DOA estimation is challenging in the…
We propose a novel multi-source direction of arrival (DOA) estimation technique using a convolutional neural network algorithm which learns the modal coherence patterns of an incident soundfield through measured spherical harmonic…