Sound Classification
62 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
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
We show that the improved performance stems from the combination of a deep, high-capacity model and an augmented training set: this combination outperforms both the proposed CNN without augmentation and a "shallow" dictionary learning model with augmentation.
AudioCLIP: Extending CLIP to Image, Text and Audio
AudioCLIP achieves new state-of-the-art results in the Environmental Sound Classification (ESC) task, out-performing other approaches by reaching accuracies of 90. 07% on the UrbanSound8K and 97. 15% on the ESC-50 datasets.
Masked Conditional Neural Networks for Environmental Sound Classification
We have evaluated the MCLNN performance using the Urbansound8k dataset of environmental sounds.
End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network
In this paper, we present an end-to-end approach for environmental sound classification based on a 1D Convolution Neural Network (CNN) that learns a representation directly from the audio signal.
Differentiable Tracking-Based Training of Deep Learning Sound Source Localizers
Data-based and learning-based sound source localization (SSL) has shown promising results in challenging conditions, and is commonly set as a classification or a regression problem.
BUET Multi-disease Heart Sound Dataset: A Comprehensive Auscultation Dataset for Developing Computer-Aided Diagnostic Systems
Addressing this, we introduce the BUET Multi-disease Heart Sound (BMD-HS) dataset - a comprehensive and meticulously curated collection of heart sound recordings.
Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks
sdeva14/eusipco17-drone-sound-detection
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This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their sound data.
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
grudloff/Salamon2017Replication
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IEEE Signal Processing Letters 2017
We show that the improved performance stems from the combination of a deep, high-capacity model and an augmented training set: this combination outperforms both the proposed CNN without augmentation and a “shallow” dictionary learning model with augmentation.
Look, Listen and Learn
marl/l3embedding
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ICCV 2017
We consider the question: what can be learnt by looking at and listening to a large number of unlabelled videos?
Utilizing Domain Knowledge in End-to-End Audio Processing
End-to-end neural network based approaches to audio modelling are generally outperformed by models trained on high-level data representations.
