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Sound Event Detection

15 papers with code · Audio

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

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Greatest papers with code

Learning Sound Event Classifiers from Web Audio with Noisy Labels

4 Jan 2019lRomul/argus-freesound

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.

SOUND EVENT DETECTION

Guided Learning Convolution System for DCASE 2019 Task 4

11 Sep 2019Kikyo-16/Sound_event_detection

In this paper, we describe in detail the system we submitted to DCASE2019 task 4: sound event detection (SED) in domestic environments.

SOUND EVENT DETECTION

Guided learning for weakly-labeled semi-supervised sound event detection

6 Jun 2019Kikyo-16/Sound_event_detection

Instead of designing a single model by considering a trade-off between the two sub-targets, we design a teacher model aiming at audio tagging to guide a student model aiming at boundary detection to learn using the unlabeled data.

AUDIO TAGGING BOUNDARY DETECTION SOUND EVENT DETECTION

Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detection

24 May 2019Kikyo-16/Sound_event_detection

In this paper, a special decision surface for the weakly-supervised sound event detection (SED) and a disentangled feature (DF) for the multi-label problem in polyphonic SED are proposed.

MULTI-LABEL CLASSIFICATION MULTIPLE INSTANCE LEARNING SOUND EVENT DETECTION

Adaptive pooling operators for weakly labeled sound event detection

26 Apr 2018marl/autopool

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.

MULTIPLE INSTANCE LEARNING SOUND EVENT DETECTION TIME SERIES

A Closer Look at Weak Label Learning for Audio Events

24 Apr 2018ankitshah009/WALNet-Weak_Label_Analysis

In this work, we first describe a CNN based approach for weakly supervised training of audio events.

AUDIO CLASSIFICATION SOUND EVENT DETECTION

Robust sound event detection in bioacoustic sensor networks

20 May 2019BirdVox/birdvoxdetect

As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise.

DATA AUGMENTATION SOUND EVENT DETECTION

Sound Event Detection with Depthwise Separable and Dilated Convolutions

2 Feb 2020dr-costas/dnd-sed

The number of the channels of the CNNs and size of the weight matrices of the RNNs have a direct effect on the total amount of parameters of the SED method, which is to a couple of millions.

SOUND EVENT DETECTION

Recurrent Neural Networks for Polyphonic Sound Event Detection in Real Life Recordings

4 Apr 2016yardencsGitHub/tf_syllable_segmentation_annotation

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).

DATA AUGMENTATION SOUND EVENT DETECTION