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Found 7 papers, 4 papers with code
STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events
Additionally, the report presents the baseline system that accompanies the dataset in the challenge with emphasis on the differences with the baseline of the previous iterations; namely, introduction of the multi-ACCDOA representation to handle multiple simultaneous occurences of events of the same class, and support for additional improved input features for the microphone array format.
Ranked #1 on
Sound Event Localization and Detection
on STARSS22
Distortion Audio Effects: Learning How to Recover the Clean Signal
Given the recent advances in music source separation and automatic mixing, removing audio effects in music tracks is a meaningful step toward developing an automated remixing system.
Multi-ACCDOA: Localizing and Detecting Overlapping Sounds from the Same Class with Auxiliary Duplicating Permutation Invariant Training
The multi- ACCDOA format (a class- and track-wise output format) enables the model to solve the cases with overlaps from the same class.
Spatial mixup: Directional loudness modification as data augmentation for sound event localization and detection
Data augmentation methods have shown great importance in diverse supervised learning problems where labeled data is scarce or costly to obtain.
Ensemble of ACCDOA- and EINV2-based Systems with D3Nets and Impulse Response Simulation for Sound Event Localization and Detection
This report describes our systems submitted to the DCASE2021 challenge task 3: sound event localization and detection (SELD) with directional interference.
ACCDOA: Activity-Coupled Cartesian Direction of Arrival Representation for Sound Event Localization and Detection
Conventional NN-based methods use two branches for a sound event detection (SED) target and a direction-of-arrival (DOA) target.
Metric Learning with Background Noise Class for Few-shot Detection of Rare Sound Events
Few-shot learning systems for sound event recognition have gained interests since they require only a few examples to adapt to new target classes without fine-tuning.
