We present the Latent Timbre Synthesis, a new audio synthesis method using deep learning. The synthesis method allows composers and sound designers to interpolate and extrapolate between the timbre of multiple sounds using the latent space of audio frames. We provide the details of two Variational Autoencoder architectures for the Latent Timbre Synthesis and compare their advantages and drawbacks. The implementation includes a fully working application with a graphical user interface, called interpolate_two, which enables practitioners to generate timbres between two audio excerpts of their selection using interpolation and extrapolation in the latent space of audio frames. Our implementation is open source, and we aim to improve the access...
The problem of automatic music transcription (AMT) is considered by many researchers as the holy gra...
Computer assisted music extensively relies on audio sample libraries and virtual instruments which p...
Synthesizers have been an essential tool for composers of any style of music including computer gene...
The research in Deep Learning applications in sound and music computing have gathered an interest in...
Controllable timbre synthesis has been a subject of research for several decades, and deep neural ne...
In the past years, several hybridization techniques have been proposed to synthesize novel audio con...
In this work we propose a deep learning based method—namely, variational, convolutional recurrent au...
How can we provide interfaces to synthesis algorithms thatwill allow us to manipulate timbre directl...
In this paper, we learn disentangled representations of timbre and pitch for musical instrument soun...
A deformable musical instrument can take numerous distinct shapes with its non-rigid features. Build...
Generative models aim to understand the properties of data, through the construction of latent space...
Special issue on Deep learning for music and audioInternational audienceIn addition to traditional t...
The aim of the thesis is the design and evaluation of a generative model based on deep learning for ...
Variational Autoencoders (VAEs) constitute one of the most significant deep generative models for th...
Automatic music modelling and generation is a challenging task. The ability to learn from big data c...
The problem of automatic music transcription (AMT) is considered by many researchers as the holy gra...
Computer assisted music extensively relies on audio sample libraries and virtual instruments which p...
Synthesizers have been an essential tool for composers of any style of music including computer gene...
The research in Deep Learning applications in sound and music computing have gathered an interest in...
Controllable timbre synthesis has been a subject of research for several decades, and deep neural ne...
In the past years, several hybridization techniques have been proposed to synthesize novel audio con...
In this work we propose a deep learning based method—namely, variational, convolutional recurrent au...
How can we provide interfaces to synthesis algorithms thatwill allow us to manipulate timbre directl...
In this paper, we learn disentangled representations of timbre and pitch for musical instrument soun...
A deformable musical instrument can take numerous distinct shapes with its non-rigid features. Build...
Generative models aim to understand the properties of data, through the construction of latent space...
Special issue on Deep learning for music and audioInternational audienceIn addition to traditional t...
The aim of the thesis is the design and evaluation of a generative model based on deep learning for ...
Variational Autoencoders (VAEs) constitute one of the most significant deep generative models for th...
Automatic music modelling and generation is a challenging task. The ability to learn from big data c...
The problem of automatic music transcription (AMT) is considered by many researchers as the holy gra...
Computer assisted music extensively relies on audio sample libraries and virtual instruments which p...
Synthesizers have been an essential tool for composers of any style of music including computer gene...