MULTICHANNEL AUDIO CLASSIFICATION WITH NEURAL NETWORKS USING SCATTERING TRANSFORM
This technical paper presents an approach for the 2018 acoustic scene classification challenge (DCASE 2018) task 5. A sequence of audio segments are observed by an array with 4 microphones. The task is to suggest a multichannel processing to classify the audio signals to one of 9 pre defined classes. The proposed approach combines a deep neural network with scattering transform. Each audio segment is first represented by two layers of scattering transform. The 4 denoised transforms of each of the two layers are combined together. Each of the fused layers are processed in parallel by two neural networks (NN) architectures, RESNET and long short term memory (LSTM) network, with a joint fully connected layer.