Sound Event Detection
60 papers with code • 4 benchmarks • 18 datasets
Sound Event Detection (SED) is the task of recognizing the sound events and their respective temporal start and end time in a recording. Sound events in real life do not always occur in isolation, but tend to considerably overlap with each other. Recognizing such overlapping sound events is referred as polyphonic SED.
Source: A report on sound event detection with different binaural features
Libraries
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Most implemented papers
Towards Deep Learning Models Resistant to Adversarial Attacks
Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal.
PHNNs: Lightweight Neural Networks via Parameterized Hypercomplex Convolutions
In this paper, we define the parameterization of hypercomplex convolutional layers and introduce the family of parameterized hypercomplex neural networks (PHNNs) that are lightweight and efficient large-scale models.
Recurrent Neural Networks for Polyphonic Sound Event Detection in Real Life Recordings
In this paper we present an approach to polyphonic sound event detection in real life recordings based on bi-directional long short term memory (BLSTM) recurrent neural networks (RNNs).
Adaptive pooling operators for weakly labeled sound event detection
In this work, we treat SED as a multiple instance learning (MIL) problem, where training labels are static over a short excerpt, indicating the presence or absence of sound sources but not their temporal locality.
Learning Sound Event Classifiers from Web Audio with Noisy Labels
To foster the investigation of label noise in sound event classification we present FSDnoisy18k, a dataset containing 42. 5 hours of audio across 20 sound classes, including a small amount of manually-labeled data and a larger quantity of real-world noisy data.
SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks
The understanding of the surrounding environment plays a critical role in autonomous robotic systems, such as self-driving cars.
ACCDOA: Activity-Coupled Cartesian Direction of Arrival Representation for Sound Event Localization and Detection
Conventional NN-based methods use two branches for a sound event detection (SED) target and a direction-of-arrival (DOA) target.
Couple Learning for semi-supervised sound event detection
The recently proposed Mean Teacher method, which exploits large-scale unlabeled data in a self-ensembling manner, has achieved state-of-the-art results in several semi-supervised learning benchmarks.
RCT: Random Consistency Training for Semi-supervised Sound Event Detection
Sound event detection (SED), as a core module of acoustic environmental analysis, suffers from the problem of data deficiency.
Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks
This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their sound data.