From Words to Sound: Neural Audio Synthesis of Guitar Sounds with Timbral Descriptors
Interest in neural audio synthesis has been growing lately both in academia and industry. Deep Learning (DL) synthesisers enable musicians to generate fresh, often completely unconventional sounds. However, most of these applications present a drawback. It is difficult for musicians to generate sounds which reflect the timbral properties they have in mind, because of the nature of the latent spaces of such systems. These spaces generally have large dimensionality and cannot easily be mapped to semantically meaningful timbral properties. Navigation of such timbral spaces is therefore impractical. In this paper, we introduce a DL-powered instrument that generates guitar sounds from vocal commands. The system analyses vocal instructions to extract timbral descriptors which condition the sound generation.
Code (1)
Tasks
Audio SynthesisSimilar Papers 제목 키워드 기반
Expressive Acoustic Guitar Sound Synthesis with an Instrument-Specific Input Representation and Diffusion Outpainting
Synthesizing performing guitar sound is a highly challenging task due to the polyphony and high variability in expression. Recently, deep generative models have shown promising results in synthesizing expressive polyphon…
GuitarFlow: Realistic Electric Guitar Synthesis From Tablatures via Flow Matching and Style Transfer
Music generation in the audio domain using artificial intelligence (AI) has witnessed steady progress in recent years. However for some instruments, particularly the guitar, controllable instrument synthesis remains limi…
Music GenerationStyle TransferThe VU Sound Corpus: Adding More Fine-grained Annotations to the Freesound Database
This paper presents a collection of annotations (tags or keywords) for a set of 2,133 environmental sounds taken from the Freesound database (www.freesound.org). The annotations are acquired through an open-ended crowd-l…
Text-Driven Separation of Arbitrary Sounds
We propose a method of separating a desired sound source from a single-channel mixture, based on either a textual description or a short audio sample of the target source. This is achieved by combining two distinct model…
DrumGAN: Synthesis of Drum Sounds With Timbral Feature Conditioning Using Generative Adversarial Networks
Synthetic creation of drum sounds (e.g., in drum machines) is commonly performed using analog or digital synthesis, allowing a musician to sculpt the desired timbre modifying various parameters. Typically, such parameter…
Audio SynthesisGenerative Adversarial Network