Sound event detection using spatial features and convolutional recurrent neural network

Adavanne, Sharath; Pertilä, Pasi; Virtanen, Tuomas

doi:10.1109/icassp.2017.7952260

Cited by 126 publications

(101 citation statements)

References 25 publications

(44 reference statements)

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“…We acquired the strong labels of most of the training dataset recordings via manual annotations, to be used only for evaluation purposes. 2 For our training set the first 499 recordings of the NIPS4B 2013 training dataset are used, while the rest are included in our testing set, excluding 14 recordings for which confident strong annotations could not be attained. Those 14 recordings were added to our training set totalling to 513 training recordings and 174 testing recordings.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Audio Event Detection and Tagging on Low-Resource Datasets

Morfi

Stowell

2018

Applied Sciences

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In training a deep learning system to perform audio transcription, two practical problems may arise. Firstly, most datasets are weakly labelled, having only a list of events present in each recording without any temporal information for training. Secondly, deep neural networks need a very large amount of labelled training data to achieve good quality performance, yet in practice it is difficult to collect enough samples for most classes of interest. In this paper, we propose factorising the final task of audio transcription into multiple intermediate tasks in order to improve the training performance when dealing with this kind of low-resource datasets. We evaluate three data-efficient approaches of training a stacked convolutional and recurrent neural network for the intermediate tasks.Our results show that different methods of training have different advantages and disadvantages.

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Section: Discussionmentioning

confidence: 99%

Deep Learning for Audio Event Detection and Tagging on Low-Resource Datasets

Morfi

Stowell

2018

Applied Sciences

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“…Most recent advances in polyphonic SED are largely attributed to the use of Machine Learning and Deep Neural Networks [8,9,10,11,12,13]. In particular, the use of Convolutional Recurrent Neural Networks (CRNNs) has significantly improved SED performance in the past few years [14,15,16,17]. However, there are three main disadvantages with current CRNN-based polyphonic SED approaches.…”

Section: Related Workmentioning

confidence: 99%

Polyphonic Sound Event and Sound Activity Detection: A Multi-Task Approach

Pankajakshan

Bear

Benetos

2019

2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

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Polyphonic Sound Event Detection (SED) in real-world recordings is a challenging task because of the dynamic polyphony level, intensity, and duration of sound events. Current polyphonic SED systems fail to model the temporal structure of sound events explicitly and instead attempt to look at which sound events are present at each audio frame. Consequently, the event-wise detection performance is much lower than the segment-wise detection performance. In this work, we propose a joint model approach to improve the temporal localization of sound events using a multi-task learning setup. The first task predicts which sound events are present at each time frame; we call this branch 'Sound Event Detection (SED) model', while the second task predicts if a sound event is present or not at each frame; we call this branch 'Sound Activity Detection (SAD) model'. We verify the proposed joint model by comparing it with a separate implementation of both tasks aggregated together from individual task predictions. Our experiments on the URBAN-SED dataset show that the proposed joint model can alleviate False Positive (FP) and False Negative (FN) errors and improve both the segment-wise and the event-wise metrics.

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“…In this paper, we want to exploit the capabilities of both QNNs and Ambisonics to analyze 3D sounds, and in particular we focus on the localization and detection of 3D sound events. Both tasks have been widely investigated recently by using convolutional neural networks (CNNs) [19][20][21][22][23][24][25]. They are also considered as a joint task in [26] for 3D sounds, but considering each microphone signal as a separate real-valued signal.…”

Section: Introductionmentioning

confidence: 99%

Quaternion Convolutional Neural Networks for Detection and Localization of 3D Sound Events

Comminiello

Lella

Scardapane

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Learning from data in the quaternion domain enables us to exploit internal dependencies of 4D signals and treating them as a single entity. One of the models that perfectly suits with quaternion-valued data processing is represented by 3D acoustic signals in their spherical harmonics decomposition. In this paper, we address the problem of localizing and detecting sound events in the spatial sound field by using quaternion-valued data processing. In particular, we consider the spherical harmonic components of the signals captured by a first-order ambisonic microphone and process them by using a quaternion convolutional neural network. Experimental results show that the proposed approach exploits the correlated nature of the ambisonic signals, thus improving accuracy results in 3D sound event detection and localization.

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Sound event detection using spatial features and convolutional recurrent neural network

Cited by 126 publications

References 25 publications

Deep Learning for Audio Event Detection and Tagging on Low-Resource Datasets

Deep Learning for Audio Event Detection and Tagging on Low-Resource Datasets

Polyphonic Sound Event and Sound Activity Detection: A Multi-Task Approach

Quaternion Convolutional Neural Networks for Detection and Localization of 3D Sound Events

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