Direction of Arrival Estimation
8 papers with code • 1 benchmarks • 3 datasets
Estimating the direction-of-arrival (DOA) of a sound source from multi-channel recordings.
Data-based and learning-based sound source localization (SSL) has shown promising results in challenging conditions, and is commonly set as a classification or a regression problem.
In this paper, we present a new model for Direction of Arrival (DOA) estimation of sound sources based on an Icosahedral Convolutional Neural Network (CNN) applied over SRP-PHAT power maps computed from the signals received by a microphone array.
Regression and Classification for Direction-of-Arrival Estimation with Convolutional Recurrent Neural Networks
We present a novel learning-based approach to estimate the direction-of-arrival (DOA) of a sound source using a convolutional recurrent neural network (CRNN) trained via regression on synthetic data and Cartesian labels.
Refinement of Direction of Arrival Estimators by Majorization-Minimization Optimization on the Array Manifold
We propose a generalized formulation of direction of arrival estimation that includes many existing methods such as steered response power, subspace, coherent and incoherent, as well as speech sparsity-based methods.
DCASE 2021 Task 3: Spectrotemporally-aligned Features for Polyphonic Sound Event Localization and Detection
Sound event localization and detection consists of two subtasks which are sound event detection and direction-of-arrival estimation.
Sound event localization and detection (SELD) is an emerging research topic that aims to unify the tasks of sound event detection and direction-of-arrival estimation.
SALSA: Spatial Cue-Augmented Log-Spectrogram Features for Polyphonic Sound Event Localization and Detection
Sound event localization and detection (SELD) consists of two subtasks, which are sound event detection and direction-of-arrival estimation.