32 research outputs found
TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer
In this work, we address the problem of musical timbre transfer, where the
goal is to manipulate the timbre of a sound sample from one instrument to match
another instrument while preserving other musical content, such as pitch,
rhythm, and loudness. In principle, one could apply image-based style transfer
techniques to a time-frequency representation of an audio signal, but this
depends on having a representation that allows independent manipulation of
timbre as well as high-quality waveform generation. We introduce TimbreTron, a
method for musical timbre transfer which applies "image" domain style transfer
to a time-frequency representation of the audio signal, and then produces a
high-quality waveform using a conditional WaveNet synthesizer. We show that the
Constant Q Transform (CQT) representation is particularly well-suited to
convolutional architectures due to its approximate pitch equivariance. Based on
human perceptual evaluations, we confirmed that TimbreTron recognizably
transferred the timbre while otherwise preserving the musical content, for both
monophonic and polyphonic samples.Comment: 17 pages, published as a conference paper at ICLR 201
Generative models for music using transformer architectures
openThis thesis focus on growth and impact of Transformes architectures which are mainly used for Natural Language Processing tasks for Audio generation. We think that music, with its notes, chords, and volumes, is a language. You could think of symbolic representation of music as human language.
A brief sound synthesis history which gives basic foundation for modern AI-generated music models is mentioned . The most recent in AI-generated audio is carefully studied and instances of AI-generated music is told in many contexts. Deep learning models and their applications to real-world issues are one of the key subjects that are covered.
The main areas of interest include transformer-based audio generation, including the training procedure, encoding and decoding techniques, and post-processing stages. Transformers have several key advantages, including long-term consistency and the ability to create minute-long audio compositions.
Numerous studies on the various representations of music have been explained, including how neural network and deep learning techniques can be used to apply symbolic melodies, musical arrangements, style transfer, and sound production.
This thesis largely focuses on transformation models, but it also recognises the importance of numerous AI-based generative models, including GAN.
Overall, this thesis enhances generative models for music composition and provides a complete understanding of transformer design. It shows the possibilities of AI-generated sound synthesis by emphasising the most current developments.This thesis focus on growth and impact of Transformes architectures which are mainly used for Natural Language Processing tasks for Audio generation. We think that music, with its notes, chords, and volumes, is a language. You could think of symbolic representation of music as human language.
A brief sound synthesis history which gives basic foundation for modern AI-generated music models is mentioned . The most recent in AI-generated audio is carefully studied and instances of AI-generated music is told in many contexts. Deep learning models and their applications to real-world issues are one of the key subjects that are covered.
The main areas of interest include transformer-based audio generation, including the training procedure, encoding and decoding techniques, and post-processing stages. Transformers have several key advantages, including long-term consistency and the ability to create minute-long audio compositions.
Numerous studies on the various representations of music have been explained, including how neural network and deep learning techniques can be used to apply symbolic melodies, musical arrangements, style transfer, and sound production.
This thesis largely focuses on transformation models, but it also recognises the importance of numerous AI-based generative models, including GAN.
Overall, this thesis enhances generative models for music composition and provides a complete understanding of transformer design. It shows the possibilities of AI-generated sound synthesis by emphasising the most current developments
MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer
We introduce MIDI-VAE, a neural network model based on Variational
Autoencoders that is capable of handling polyphonic music with multiple
instrument tracks, as well as modeling the dynamics of music by incorporating
note durations and velocities. We show that MIDI-VAE can perform style transfer
on symbolic music by automatically changing pitches, dynamics and instruments
of a music piece from, e.g., a Classical to a Jazz style. We evaluate the
efficacy of the style transfer by training separate style validation
classifiers. Our model can also interpolate between short pieces of music,
produce medleys and create mixtures of entire songs. The interpolations
smoothly change pitches, dynamics and instrumentation to create a harmonic
bridge between two music pieces. To the best of our knowledge, this work
represents the first successful attempt at applying neural style transfer to
complete musical compositions.Comment: Paper accepted at the 19th International Society for Music
Information Retrieval Conference, ISMIR 2018, Paris, Franc
Composer Style-specific Symbolic Music Generation Using Vector Quantized Discrete Diffusion Models
Emerging Denoising Diffusion Probabilistic Models (DDPM) have become
increasingly utilised because of promising results they have achieved in
diverse generative tasks with continuous data, such as image and sound
synthesis. Nonetheless, the success of diffusion models has not been fully
extended to discrete symbolic music. We propose to combine a vector quantized
variational autoencoder (VQ-VAE) and discrete diffusion models for the
generation of symbolic music with desired composer styles. The trained VQ-VAE
can represent symbolic music as a sequence of indexes that correspond to
specific entries in a learned codebook. Subsequently, a discrete diffusion
model is used to model the VQ-VAE's discrete latent space. The diffusion model
is trained to generate intermediate music sequences consisting of codebook
indexes, which are then decoded to symbolic music using the VQ-VAE's decoder.
The results demonstrate our model can generate symbolic music with target
composer styles that meet the given conditions with a high accuracy of 72.36%
DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models
Originating in the Renaissance and burgeoning in the digital era, tablatures are a commonly used music notation system which provides explicit representations of instrument fingerings rather than pitches. GuitarPro has established itself as a widely used tablature format and software enabling musicians to edit and share songs for musical practice, learning, and composition. In this work, we present DadaGP, a new symbolic music dataset comprising 26,181 song scores in the GuitarPro format covering 739 musical genres, along with an accompanying tokenized format well-suited for generative sequence models such as the Transformer. The tokenized format is inspired by event-based MIDI encodings, often used in symbolic music generation models. The dataset is released with an encoder/decoder which converts GuitarPro files to tokens and back. We present results of a use case in which DadaGP is used to train a Transformer-based model to generate new songs in GuitarPro format. We discuss other relevant use cases for the dataset (guitar-bass transcription, music style transfer and artist/genre classification) as well as ethical implications. DadaGP opens up the possibility to train GuitarPro score generators, fine-tune models on custom data, create new styles of music, AI-powered songwriting apps, and human-AI improvisation
Toward Interactive Music Generation: A Position Paper
Music generation using deep learning has received considerable attention in recent years. Researchers have developed various generative models capable of imitating musical conventions, comprehending the musical corpora, and generating new samples based on the learning outcome. Although the samples generated by these models are persuasive, they often lack musical structure and creativity. For instance, a vanilla end-to-end approach, which deals with all levels of music representation at once, does not offer human-level control and interaction during the learning process, leading to constrained results. Indeed, music creation is a recurrent process that follows some principles by a musician, where various musical features are reused or adapted. On the other hand, a musical piece adheres to a musical style, breaking down into precise concepts of timbre style, performance style, composition style, and the coherency between these aspects. Here, we study and analyze the current advances in music generation using deep learning models through different criteria. We discuss the shortcomings and limitations of these models regarding interactivity and adaptability. Finally, we draw the potential future research direction addressing multi-agent systems and reinforcement learning algorithms to alleviate these shortcomings and limitations
Music-STAR: a Style Translation system for Audio-based Rearrangement
Music style translation has recently gained attention among music processing
studies. It aims to generate variations of existing music pieces by altering the style-variant characteristics of the original music piece, while content such as the melody
remains unchanged. These alterations could involve timbre translation, reharmonization,
or music rearrangement.
In this thesis, we plan to address music rearrangement, focusing on instrumentation, by processing waveforms of two-instrument pieces. Previous studies have achieved promising results utilizing time-frequency and symbolic music representations. Music translation on raw audio has also been investigated using single-instrument pieces. Although processing raw audio is more challenging, it embodies more detailed information about the performance, timbre, and dynamics of a music piece. To this end, we introduce Music-STAR, the first audio-based model that can transform the instruments of a multi-track piece into another set of instruments, resulting in a rearranged piece