Title: “Style” Transfer for Musical Audio Using Multiple Time-Frequency Representations. Abstract: 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.