Benchmarks
Datasets
Greatest papers with code
PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
arXiv 2020
• qiuqiangkong/audioset_tagging_cnn
• 
However, there is limited research on pretraining neural networks on large datasets for audio pattern recognition.
ACOUSTIC SCENE CLASSIFICATION AUDIO TAGGING SCENE CLASSIFICATION SOUND EVENT DETECTION
musicnn: Pre-trained convolutional neural networks for music audio tagging
Pronounced as "musician", the musicnn library contains a set of pre-trained musically motivated convolutional neural networks for music audio tagging: https://github. com/jordipons/musicnn.
Audio tagging with noisy labels and minimal supervision
The task evaluates systems for multi-label audio tagging using a large set of noisy-labeled data, and a much smaller set of manually-labeled data, under a large vocabulary setting of 80 everyday sound classes.
Weakly labelled audioset tagging with attention neural networks
IEEE/ACM Transactions on Audio, Speech, and Language Processing 2019
• qiuqiangkong/audioset_classification
• 
We bridge the connection between attention neural networks and multiple instance learning (MIL) methods, and propose decision-level and feature-level attention neural networks for audio tagging.
Speech Denoising Convolutional Neural Network trained with Deep Feature Losses.
Interspeech 2018
• francoisgermain/SpeechDenoisingWithDeepFeatureLosses
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We present an end-to-end deep learning approach to denoising speech signals by processing the raw waveform directly.
General audio tagging with ensembling convolutional neural network and statistical features
Audio tagging is challenging due to the limited size of data and noisy labels.
DCASENET: A joint pre-trained deep neural network for detecting and classifying acoustic scenes and events
• Jungjee/DcaseNet
•
Single task deep neural networks that perform a target task among diverse cross-related tasks in the acoustic scene and event literature are being developed.
ACOUSTIC SCENE CLASSIFICATION AUDIO TAGGING SOUND EVENT DETECTION AUDIO AND SPEECH PROCESSING
Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging
For the unsupervised feature learning, we propose to use a symmetric or asymmetric deep de-noising auto-encoder (sDAE or aDAE) to generate new data-driven features from the Mel-Filter Banks (MFBs) features.
CRNNs for Urban Sound Tagging with spatiotemporal context
• multitel-ai/urban-sound-tagging
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This paper describes CRNNs we used to participate in Task 5 of the DCASE 2020 challenge.
Urban Sound Classification : striving towards a fair comparison
• multitel-ai/urban-sound-classification-and-comparison
•
Sometimes authors copy-pasting the results of the original papers which is not helping reproducibility.