Deep Convolutional Neural Networks For Environmental Sound Classification
Abhishek NigudgiAnkush AwantySuvarna S. NandyaS ChuS NarayananC.-C KuoR RadhakrishnanA DivakaranP SmaragdisC MydlarzJ SalamonJ BelloA MesarosT HeittolaO DikmenT VirtanenE BenetosG LafayM LagrangeM PlumbleyV BisotR SerizelS EssidG RichardJ SalamonJ BelloJ GeigerK HelwaniE CakirT HeittolaH HuttunenT VirtanenK PiczakD GiannoulisE BenetosD StowellM RossignolM LagrangeM PlumbleyD StowellD GiannoulisE BenetosM LagrangeM PlumbleyS SigtiaA StarkS KrstulovicM Plumbley
Abstract
Abstract: We propose a model to classify environmental sounds such as People Sounds, Vehicles Sounds, Siren Sounds, Horn, Engine Sounds. We perform Data Augmentation techniques to extract best features from the given audio to classify which class of sound. Our deep convolutional neural network architecture uses stacked convolutional and pooling layers to extract highlevel feature representations from spectrogram-like features from the given input.
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