In this article, we investigate the notion of model-based deep learning in the realm of music
information research (MIR). Loosely speaking, we refer to the term model-based deep …

Music mixing is compositional--experts combine multiple audio processors to achieve a
cohesive mix from dry source tracks. We propose a method to reverse engineer this process …

Sound matching algorithms seek to approximate a target waveform by parametric audio
synthesis. Deep neural networks have achieved promising results in matching sustained …

Sound retrieval for assisted music composition depends on evaluating similarity between
musical instrument sounds, which is partly influenced by playing techniques. Previous …

Automatic tuning of reverberation algorithms relies on the optimization of a cost function.
While general audio similarity metrics are useful, they are not optimized for the specific …

Perceptual sound matching (PSM) aims to find the input parameters to a synthesizer so as to
best imitate an audio target. Deep learning for PSM optimizes a neural network to analyze …

In the field of intelligent audio production, neural networks have been trained to
automatically mix a multitrack to a stereo mixdown. Although these algorithms contain latent …

In media production, the sound design process often involves the use of pre-recorded sound
samples as the source of the audio assets. However, the increasing size and complexity of …

Musician and instrument make up a central duo in the musical experience. Inseparable, they
are the key actors of the musical performance, transforming a composition into an emotional …

Electronic dance music (EDM), produced using computers and electronic instruments, is a
collection of musical subgenres that emphasise timbre and rhythm over melody and …