Stars
Baseline method for sound event localization task of DCASE 2023 challenge
CST-former: Transformer with Channel-Spectro-Temporal Attention for Sound Event Localization and Detection (ICASSP 2024)
This repo hosts the code and models of "Masked Autoencoders that Listen".
Official repository of the work "Low-complexity Unsupervised Audio Anomaly Detection exploiting Separable Convolutions and Angular Loss" published to IEEE Sensors Letters.
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
A spectro-temporal fusion feature, STgram, with MobileFaceNet For more stable Anomalous Sound Detection
Official code, datasets and checkpoints for "Timer: Generative Pre-trained Transformers Are Large Time Series Models" (ICML 2024)
[AAAI 2024 Oral] AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models
DCASE 2020 Task 2 - Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring
Noisy-ArcMix: Additive Noisy Angular Margin Loss Combined With Mixup for Anomalous Sound Detection
The GitHub repository for the paper "Informer" accepted by AAAI 2021.
About Code release for "Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy" (ICLR 2022 Spotlight), https://openreview.net/forum?id=LzQQ89U1qm_
About Code release for "TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis" (ICLR 2023), https://openreview.net/pdf?id=ju_Uqw384Oq
Code for the paper Hybrid Spectrogram and Waveform Source Separation
Noise suppression plugin based on Xiph's RNNoise
A self-supervised speech denoising strategy named Only-Noisy Training (ONT), which solves the speech denoising problem with only noisy audio signals in audio space for the first time.
Audio-Visual Speech Enhancement Challenge (AVSE) 2024
Score-based Generative Models (Diffusion Models) for Speech Enhancement and Dereverberation
Applied generative adversarial networks (GANs) to do anomaly detection for time series data
Convolutional Neural Network(CNN) feature extraction from Mel-Spectrograms to predict laboratory induced faults in a signal.