Code for the paper "Self-Supervised Learning for Anomalous Sound Detection"
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Updated
May 13, 2024 - Python
Code for the paper "Self-Supervised Learning for Anomalous Sound Detection"
Code for the paper "AdaProj: Adaptively Scaled Angular Margin Subspace Projections for Anomalous Sound Detection with Auxiliary Classification Tasks"
Implementation of the threshold-independent performance measure F1-EV for semi-supervised anomaly detection.
Accompanying code for the paper Design Choices for Learning Embeddings from Auxiliary Tasks for Domain Generalization in Anomalous Sound Detection.
Accompanying code for the paper On Using Pre-Trained Embeddings for Detecting Anomalous Sounds with Limited Training Data.
Audio captioning baseline system for DCASE 2020 challenge.
OpenL3: Open-source deep audio and image embeddings
Submission for task 2 "First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring" of the DCASE challenge 2023 (https://dcase.community/challenge2023/task-first-shot-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring)
Deep neural network model combining audio signal processing and pre-trained audio CNN achieved 90.1% adjusted accuracy (27.6% improvement) for classifying audio recording environment.
DCASE2020 Challenge Task 2 baseline system
Identify a wide variety of bird vocalizations in soundscape recordings
Codes related to acoustic scene classification task for DCASE 2022
A library for soundscape synthesis and augmentation
Codes related to DCASE2021 Task 1 - Acoustic Scene Classification
Autoencoder-based baseline system for DCASE2021 Challenge Task 2.
MobileNetV2-based baseline system for DCASE2021 Challenge Task 2.
Unsupervised Domain Adaptation for Acoustic Scene Classification with Wasserstein Distance
Harmony and Timbre-Oriented MIR Framework
Sound event detection with depthwise separable and dilated convolutions.
Code for using with the Clotho dataset
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