Title: SpectroBank: A filter-bank convolutional layer for CNN-based audio applications. Abstract: We propose and investigate the design of a new convolutional layer where kernels are parameterized functions. This layer aims at being the input layer of convolutional neural networks for audio applications. The kernels are defined as functions having a band-pass filter shape, with a limited number of trainable parameters. We show that networks having such an input layer can achieve state-of-the-art accuracy on several audio classification tasks. This approach, while reducing the number of weights to be trained along with network training time, enables larger kernel sizes, an advantage for audio applications. Furthermore, the learned filters bring additional interpretability and a better understanding of the data properties exploited by the network.