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Found 15 papers, 8 papers with code
Position tracking of a varying number of sound sources with sliding permutation invariant training
Recent data- and learning-based sound source localization (SSL) methods have shown strong performance in challenging acoustic scenarios.
STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events
Additionally, the report presents the baseline system that accompanies the dataset in the challenge with emphasis on the differences with the baseline of the previous iterations; namely, introduction of the multi-ACCDOA representation to handle multiple simultaneous occurences of events of the same class, and support for additional improved input features for the microphone array format.
Ranked #1 on
Sound Event Localization and Detection
on STARSS22
Self-supervised Learning of Audio Representations from Audio-Visual Data using Spatial Alignment
In addition to the correspondence, AVSA also learns from the spatial location of acoustic and visual content.
Differentiable Tracking-Based Training of Deep Learning Sound Source Localizers
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.
Joint Direction and Proximity Classification of Overlapping Sound Events from Binaural Audio
Finally, we propose various ways of combining the proximity and direction estimation problems into a joint task providing temporal information about the onsets and offsets of the appearing sources.
Mobile Microphone Array Speech Detection and Localization in Diverse Everyday Environments
Joint sound event localization and detection (SELD) is an integral part of developing context awareness into communication interfaces of mobile robots, smartphones, and home assistants.
Deep neural network Based Low-latency Speech Separation with Asymmetric analysis-Synthesis Window Pair
Time-frequency masking or spectrum prediction computed via short symmetric windows are commonly used in low-latency deep neural network (DNN) based source separation.
A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection
This report presents the dataset and baseline of Task 3 of the DCASE2021 Challenge on Sound Event Localization and Detection (SELD).
Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019
A large-scale realistic dataset of spatialized sound events was generated for the challenge, to be used for training of learning-based approaches, and for evaluation of the submissions in an unlabeled subset.
A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection
This report presents the dataset and the evaluation setup of the Sound Event Localization & Detection (SELD) task for the DCASE 2020 Challenge.
A multi-room reverberant dataset for sound event localization and detection
This paper presents the sound event localization and detection (SELD) task setup for the DCASE 2019 challenge.
Sound Audio and Speech Processing
Localization, Detection and Tracking of Multiple Moving Sound Sources with a Convolutional Recurrent Neural Network
This paper investigates the joint localization, detection, and tracking of sound events using a convolutional recurrent neural network (CRNN).
Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks
In this paper, we propose a convolutional recurrent neural network for joint sound event localization and detection (SELD) of multiple overlapping sound events in three-dimensional (3D) space.
Sound Audio and Speech Processing
Multichannel Sound Event Detection Using 3D Convolutional Neural Networks for Learning Inter-channel Features
Each of this dataset has a four-channel first-order Ambisonic, binaural, and single-channel versions, on which the performance of SED using the proposed method are compared to study the potential of SED using multichannel audio.
Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network
This paper proposes a deep neural network for estimating the directions of arrival (DOA) of multiple sound sources.
