Sound Classification
46 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Sound Classification
InfantMarmosetsVox
Audio de mosquitos Aedes Aegypti
Most implemented papers
Urban Sound Tagging using Convolutional Neural Networks
sainathadapa/urban-sound-tagging
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The proposed model uses log-scaled Mel-spectrogram as the representation format for the audio data.
Spectrogram-frame linear network and continuous frame sequence for bird sound classification
MeetXinZhang/Spectrogram_frame-linear-network
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Ecological Informatics 2019
Inspired by that bird sound has various frequency distributions and continuous time-varying properties, a novel method is proposed for the classification of bird sound based on continuous frame sequence and spectrogram-frame linear network (SFLN).
LungBRN: A Smart Digital Stethoscope for Detecting Respiratory Disease Using bi-ResNet Deep Learning Algorithm
SJTU-YONGFU-RESEARCH-GRP/Lung-Sound-Classification-System-LungSys-I
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IEEE Biomedical Circuits and Systems (BIOCAS) 2019
Improving access to health care services for the medically under-served population is vital to ensure that critical illness can be addressed immediately.
ESResNet: Environmental Sound Classification Based on Visual Domain Models
Environmental Sound Classification (ESC) is an active research area in the audio domain and has seen a lot of progress in the past years.
Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning
We also implement a patient specific model tuning strategy that first screens respiratory patients and then builds patient specific classification models using limited patient data for reliable anomaly detection.
Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis
We believe that insights from analysis of Coswara can be effective in enabling sound based technology solutions for point-of-care diagnosis of respiratory infection, and in the near future this can help to diagnose COVID-19.
Urban Sound Classification : striving towards a fair comparison
multitel-ai/urban-sound-classification-and-comparison
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Sometimes authors copy-pasting the results of the original papers which is not helping reproducibility.
Comparison of semi-supervised deep learning algorithms for audio classification
Labbeti/SSLH
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In all but one cases, MM, RMM, and FM outperformed MT and DCT significantly, MM and RMM being the best methods in most experiments.
SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification
Our extensive benchmark experiments show that our hybrid deep network models trained with combined contrastive and cross-entropy loss achieved the state-of-the-art performance on three benchmark datasets ESC-10, ESC-50, and US8K with validation accuracies of 99. 75\%, 93. 4\%, and 86. 49\% respectively.
Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices
mohaimenz/acdnet
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Significant efforts are being invested to bring state-of-the-art classification and recognition to edge devices with extreme resource constraints (memory, speed, and lack of GPU support).
