• Corpus ID: 220403640

Improving Sound Event Detection in Domestic Environments using Sound Separation

@inproceedings{Turpault2020ImprovingSE,
  title={Improving Sound Event Detection in Domestic Environments using Sound Separation},
  author={Nicolas Turpault and Scott Wisdom and Hakan Erdogan and John R. Hershey and Romain Serizel and Eduardo Fonseca and Prem Seetharaman and Justin Salamon},
  booktitle={DCASE},
  year={2020}
}
Performing sound event detection on real-world recordings often implies dealing with overlapping target sound events and non-target sounds, also referred to as interference or noise. Until now these problems were mainly tackled at the classifier level. We propose to use sound separation as a pre-processing for sound event detection. In this paper we start from a sound separation model trained on the Free Universal Sound Separation dataset and the DCASE 2020 task 4 sound event detection baseline… 
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Sound Event Detection and Separation: A Benchmark on Desed Synthetic Soundscapes
  • Nicolas Turpault, R. Serizel, J. Salamon
  • Physics, Computer Science
    ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2021
TLDR
It is shown that temporal localization of sound events remains a challenge for SED systems and that reverberation and non-target sound events severely degrade system performance.
ANALYSIS OF THE SOUND EVENT DETECTION METHODS AND SYSTEMS
TLDR
A number of problems that are associated with the development of sound event detection systems, such as the deviation for each environment and each sound category, overlapping audio events, unreliable training data, etc are presented.
Selective Pseudo-labeling and Class-wise Discriminative Fusion for Sound Event Detection
TLDR
A novel selective pseudo-labeling approach is proposed, termed SPL, to produce high confidence separated target events from blind sound separation outputs, which are then used to fine-tune the original SED model that pre-trained on the sound mixtures in a multi-objective learning style.
ADAPTIVE FOCAL LOSS WITH DATA AUGMENTATION FOR SEMI-SUPERVISED SOUND EVENT DETECTION Technical Report
TLDR
This technical report describes the submission system for DCASE2021 Task4: sound event detection and separation in domestic environments, and proposes to use various methods such as the specaugment data augmentation method, adaptive focal loss, event specific post-processing to improve the performance.
A benchmark of state-of-the-art sound event detection systems evaluated on synthetic soundscapes
TLDR
A benchmark of submissions to Detection and Classification Acoustic Scene and Events 2021 Challenge (DCASE) Task 4 representing a sampling of the state-of-the-art in Sound Event Detection task is proposed and results show that systems adapted to provide coarse segmentation outputs are more robust to different target to non-target signal-to-noise ratio and to time localization of the original event.
SoundBeam: Target sound extraction conditioned on sound-class labels and enrollment clues for increased performance and continuous learning
TLDR
This paper introduces a TSE framework, SoundBeam, that combines the advantages of both enrollment and enrollment-based approaches, and performs an extensive evaluation of the different TSE schemes using synthesized and real mixtures, which shows the potential of Sound beam.
Improving Sound Event Detection Metrics: Insights from DCASE 2020
TLDR
This paper compares conventional event-based and segment-based criteria against the Polyphonic Sound Detection Score (PSDS)'s intersection-based criterion, over a selection of systems from DCASE 2020 Challenge Task 4.
Joint framework with deep feature distillation and adaptive focal loss for weakly supervised audio tagging and acoustic event detection
What’s all the Fuss about Free Universal Sound Separation Data?
  • Scott Wisdom, Hakan Erdogan, J. Hershey
  • Computer Science, Physics
    ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2021
TLDR
An open-source baseline separation model that can separate a variable number of sources in a mixture is introduced, based on an improved time-domain convolutional network (TDCN++), that achieves scale-invariant signal-to-noise ratio improvement (SI-SNRi) on mixtures with two to four sources.
Self-Supervised Learning from Automatically Separated Sound Scenes
TLDR
This paper explores the use of unsupervised automatic sound separation to decompose unlabeled sound scenes into multiple semantically-linked views for use in self-supervised contrastive learning and finds that learning to associate input mixtures with their automatically separated outputs yields stronger representations than past approaches that use the mixtures alone.
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