Audio event and scene recognition: A unified approach using strongly and weakly labeled data

Raj, Bhiksha; Kumar, Anurag

doi:10.1109/ijcnn.2017.7966293

Cited by 31 publications

(32 citation statements)

References 25 publications

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“…However, no measures to mitigate the effect of label noise were proposed. In [11], classifiers are learnt from weakly labeled web data, and to improve performance an approach is proposed using a small amount of strongly labeled audio along with the web data. In a recent audio tagging Kaggle competition using FSDKag-gle2018 [5], a number of approaches were proposed to deal with the label noise present.…”

Section: Introductionmentioning

confidence: 99%

Learning Sound Event Classifiers from Web Audio with Noisy Labels

Fonseca

Plakal

Ellis

et al. 2019

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

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As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but inferring labels from this metadata introduces errors due to unreliable inputs, and limitations in the mapping. There is, however, little research into the impact of these errors. 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 realworld noisy data. We characterize the label noise empirically, and provide a CNN baseline system. Experiments suggest that training with large amounts of noisy data can outperform training with smaller amounts of carefully-labeled data. We also show that noiserobust loss functions can be effective in improving performance in presence of corrupted labels.

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Section: Introductionmentioning

confidence: 99%

Learning Sound Event Classifiers from Web Audio with Noisy Labels

Fonseca

Plakal

Ellis

et al. 2019

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

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“…In MIL, labels are attached to a set of instances, called bag, rather than to individual instances within the bag. MIL has been widely applied, to areas such as audio event detection (AED) [25] [26] [27] and bird sound classification [28]. An attention-based CNN model [18] has also been used for ASC and has provided interpretations in a MIL framework.…”

Section: Multiple Instance Learningmentioning

confidence: 99%

Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events

Song¹,

Han²,

Deng³

et al. 2019

Interspeech 2019

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In this paper, we propose a new strategy for acoustic scene classification (ASC) , namely recognizing acoustic scenes through identifying distinct sound events. This differs from existing strategies, which focus on characterizing global acoustical distributions of audio or the temporal evolution of short-term audio features, without analysis down to the level of sound events. To identify distinct sound events for each scene, we formulate ASC in a multi-instance learning (MIL) framework, where each audio recording is mapped into a bag-of-instances representation.Here, instances can be seen as high-level representations for sound events inside a scene. We also propose a MIL neural networks model, which implicitly identifies distinct instances (i.e., sound events). Furthermore, we propose two specially designed modules that model the multi-temporal scale and multi-modal natures of the sound events respectively. The experiments were conducted on the official development set of the DCASE2018 Task1 Subtask B, and our best-performing model improves over the official baseline by 9.4% (68.3% vs 58.9%) in terms of classification accuracy. This study indicates that recognizing acoustic scenes by identifying distinct sound events is effective and paves the way for future studies that combine this strategy with previous ones.

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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].…”

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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Audio event and scene recognition: A unified approach using strongly and weakly labeled data

Cited by 31 publications

References 25 publications

Learning Sound Event Classifiers from Web Audio with Noisy Labels

Learning Sound Event Classifiers from Web Audio with Noisy Labels

Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events

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

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