Neural Style Transfer has become a popular technique for
 generating images of distinct artistic styles using convolutional neural networks. This
 recent success in image style transfer has raised the question of
 whether similar methods can be leveraged to alter the “style” of musical
 audio. In this work, we attempt long time-scale high-quality audio transfer
 and texture synthesis in the time-domain that captures harmonic,
 rhythmic, and timbral elements related to musical style, using examples that
 may have different lengths and musical keys. We demonstrate the ability
 to use randomly initialized convolutional neural networks to transfer
 these aspects of musical style from one piece onto another using 3
 different representations of audio: the log-magnitude of the Short Time
 Fourier Transform (STFT), the Mel spectrogram, and the Constant-Q Transform
 spectrogram. We propose using these representations as a way of
 generating and modifying perceptually significant characteristics of
 musical audio content. We demonstrate each representation's
 shortcomings and advantages over others by carefully designing
 neural network structures that complement the nature of musical audio. Finally, we show that the most
 compelling “style” transfer examples make use of an ensemble of these
 representations to help capture the varying desired characteristics of
 audio signals.
We present a long time-scale musical audio style transfer algorithm which synthesizes audio in the time-domain, but uses Time-Frequency representations of audio.