Файл:Audio deep dream- optimizing raw audio with convolutional networks Ardila-audio.pdf

DiegoArdila CinjonResnick AdamRoberts DouglasEck Google Brain, Mountain View, California, USA {ardila,cinjon,adarob,deck}@google.com

ABSTRACT

ThehallucinatoryimagesofDeepDream[8]openedupthe floodgates for a recent wave of artwork generated by neural networks. In this work, we take first steps to applying this to audio. We believe a key to solving this problem is training a deep neural network to perform a perception task on raw audio. Consequently, we have followed in the footsteps of Van den Oord et al [13] and trained a network to predict embeddings that were themselves the result of a collaborative filtering model. A key difference is that we learn features directly from the raw audio, which creates a chain of differentiable functions from raw audio to high level features. We then use gradient descent on the network to extract samples of ”dreamed” audio.

Examples are available at http://tiny_.cc/78qqdy. (удалить подчеркивание)