Music style translation aims to generate variations of existing pieces of music by altering the style-related characteristics of the original piece while content, such as the melody, remains unchanged. These alterations could involve timbre translation, re-harmonization, or music rearrangement. Previous studies have achieved promising results utilizing time-frequency and symbolic music representations. Music style translation on raw audio has also been investigated and applied to single-instrument pieces. Although processing raw audio is more challenging, it provides richer information about timbres, dynamics, and articulations.In this paper, we introduce Music-STAR, the first audio-based translation system that translates the existing inst...
Selective remixing refers to altering an existing musical composition to create something new. The p...
We present a novel task of playing level conversion: generating a music arrangement in a target diff...
In this transformation we present a rhythmically constrained audio style transfer technique for auto...
Music style translation has recently gained attention among music processing studies. It aims to ge...
Research on style transfer and domain translation has clearly demonstrated the ability of deep learn...
StarNet is an audio collection of 104 classical music pieces obtained from their corresponding free ...
Led by the success of neural style transfer on visual arts, there has been a rising trend very recen...
Musical timbre transfer is the task of re-rendering the musical content of a given source using the ...
Style transfer of polyphonic music recordings is a challenging task when considering the modeling of...
Polyphonic Automatic Music Transcription remains a challenging problem. Many studies focus on the ex...
In this transformation we present a rhythmically constrained audio style transfer technique for auto...
In this transformation we present a rhythmically constrained audio style transfer technique for auto...
We introduce MIDI-VAE, a neural network model based on Variational Autoencoders that is capable of h...
We introduce MIDI-VAE, a neural network model based on Variational Autoencoders that is capable of h...
International audienceResearch on style transfer and domain translation has clearly demonstrated the...
Selective remixing refers to altering an existing musical composition to create something new. The p...
We present a novel task of playing level conversion: generating a music arrangement in a target diff...
In this transformation we present a rhythmically constrained audio style transfer technique for auto...
Music style translation has recently gained attention among music processing studies. It aims to ge...
Research on style transfer and domain translation has clearly demonstrated the ability of deep learn...
StarNet is an audio collection of 104 classical music pieces obtained from their corresponding free ...
Led by the success of neural style transfer on visual arts, there has been a rising trend very recen...
Musical timbre transfer is the task of re-rendering the musical content of a given source using the ...
Style transfer of polyphonic music recordings is a challenging task when considering the modeling of...
Polyphonic Automatic Music Transcription remains a challenging problem. Many studies focus on the ex...
In this transformation we present a rhythmically constrained audio style transfer technique for auto...
In this transformation we present a rhythmically constrained audio style transfer technique for auto...
We introduce MIDI-VAE, a neural network model based on Variational Autoencoders that is capable of h...
We introduce MIDI-VAE, a neural network model based on Variational Autoencoders that is capable of h...
International audienceResearch on style transfer and domain translation has clearly demonstrated the...
Selective remixing refers to altering an existing musical composition to create something new. The p...
We present a novel task of playing level conversion: generating a music arrangement in a target diff...
In this transformation we present a rhythmically constrained audio style transfer technique for auto...