Towards Joint Sound Scene and Polyphonic Sound Event Recognition

Bear, Helen L.; Nolasco, Ines; Benetos, Emmanouil

doi:10.21437/interspeech.2019-2169

Cited by 30 publications

(35 citation statements)

References 15 publications

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“…Conversely, multi-label classification is the prediction of up to N labels for each test sample [10]. This has been used before with joint-task work (see [11]) which strived to undertake ASC jointly with SED 2DConv (32,(7,7)) Batch Norm MaxPool2D((5,5)) Dropout(0.3) 2DConv (64,(7,7)) Batch Norm MaxPool2D((5,5)) Dropout(0.3)…”

Section: Methodsmentioning

confidence: 99%

City Classification from Multiple Real-World Sound Scenes

Bear

Heittola

Mesaros

et al. 2019

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

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The majority of sound scene analysis work focuses on one of two clearly defined tasks: acoustic scene classification or sound event detection. Whilst this separation of tasks is useful for problem definition, they inherently ignore some subtleties of the real-world, in particular how humans vary in how they describe a scene. Some will describe the weather and features within it, others will use a holistic descriptor like 'park', and others still will use unique identifiers such as cities or names. In this paper, we undertake the task of automatic city classification to ask whether we can recognize a city from a set of sound scenes? In this problem each city has recordings from multiple scenes. We test a series of methods for this novel task and show that a simple convolutional neural network (CNN) can achieve accuracy of 50%. This is less than the acoustic scene classification task baseline in the DCASE 2018 ASC challenge on the same data. A simple adaptation to the class labels of pairing city labels with grouped scenes, accuracy increases to 52%, closer to the simpler scene classification task. Finally we also formulate the problem in a multi-task learning framework and achieve an accuracy of 56%, outperforming the aforementioned approaches.

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Section: Methodsmentioning

confidence: 99%

City Classification from Multiple Real-World Sound Scenes

Bear

Heittola

Mesaros

et al. 2019

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

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“…A natural approach is to train one model to perform ASC and AED in a joint manner [76] as acoustic events are the building blocks of acoustic scenes. Sound events and acoustic scenes naturally follow a hierarchical relationship.…”

Section: Multitask Learningmentioning

confidence: 99%

A Review of Deep Learning Based Methods for Acoustic Scene Classification

Abeßer

2020

Applied Sciences

123

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The number of publications on acoustic scene classification (ASC) in environmental audio recordings has constantly increased over the last few years. This was mainly stimulated by the annual Detection and Classification of Acoustic Scenes and Events (DCASE) competition with its first edition in 2013. All competitions so far involved one or multiple ASC tasks. With a focus on deep learning based ASC algorithms, this article summarizes and groups existing approaches for data preparation, i.e., feature representations, feature pre-processing, and data augmentation, and for data modeling, i.e., neural network architectures and learning paradigms. Finally, the paper discusses current algorithmic limitations and open challenges in order to preview possible future developments towards the real-life application of ASC systems.

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“…However, some sound events and scenes are closely related, and the knowledge of sound events and scenes can help in estimating them mutually. Considering this idea, joint analysis of sound events and acoustic scenes based on MTL of SED and ASC has been proposed [18,19]. As shown in Fig.…”

Section: Joint Analysis Of Sound Events and Scenes Based On Multitaskmentioning

confidence: 99%

Sound Event Detection by Multitask Learning of Sound Events and Scenes with Soft Scene Labels

Imoto

Tonami

Yasuda

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Sound event detection (SED) and acoustic scene classification (ASC) are major tasks in environmental sound analysis. Considering that sound events and scenes are closely related to each other, some works have addressed joint analyses of sound events and acoustic scenes based on multitask learning (MTL), in which the knowledge of sound events and scenes can help in estimating them mutually. The conventional MTL-based methods utilize one-hot scene labels to train the relationship between sound events and scenes; thus, the conventional methods cannot model the extent to which sound events and scenes are related. However, in the real environment, common sound events may occur in some acoustic scenes; on the other hand, some sound events occur only in a limited acoustic scene. In this paper, we thus propose a new method for SED based on MTL of SED and ASC using the soft labels of acoustic scenes, which enable us to model the extent to which sound events and scenes are related. Experiments conducted using TUT Sound Events 2016/2017 and TUT Acoustic Scenes 2016 datasets show that the proposed method improves the SED performance by 3.80% in F-score compared with conventional MTL-based SED.

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Towards Joint Sound Scene and Polyphonic Sound Event Recognition

Cited by 30 publications

References 15 publications

City Classification from Multiple Real-World Sound Scenes

City Classification from Multiple Real-World Sound Scenes

A Review of Deep Learning Based Methods for Acoustic Scene Classification

Sound Event Detection by Multitask Learning of Sound Events and Scenes with Soft Scene Labels

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