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Kyle Kastner

Alumni

Publications

Adaptive Accompaniment with ReaLchords
Yusong Wu
Tim Cooijmans
Kyle Kastner
Adam Roberts
Ian Simon
Alexander Scarlatos
Chris Donahue
Cassie Tarakajian
Shayegan Omidshafiei
Aaron Courville
Pablo Samuel Castro
Natasha Jaques
Anna (Cheng-Zhi) Huang
Jamming requires coordination, anticipation, and collaborative creativity between musicians. Current generative models of music produce expr… (voir plus)essive output but are not able to generate in an online manner, meaning simultaneously with other musicians (human or otherwise). We propose ReaLchords, an online generative model for improvising chord accompaniment to user melody. We start with an online model pretrained by maximum likelihood, and use reinforcement learning to finetune the model for online use. The finetuning objective leverages both a novel reward model that provides feedback on both harmonic and temporal coherency between melody and chord, and a divergence term that implements a novel type of distillation from a teacher model that can see the future melody. Through quantitative experiments and listening tests, we demonstrate that the resulting model adapts well to unfamiliar input and produce fitting accompaniment. ReaLchords opens the door to live jamming, as well as simultaneous co-creation in other modalities.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
openreview.net
R-MelNet: Reduced Mel-Spectral Modeling for Neural TTS
Kyle Kastner
Aaron Courville
2022-06-30
ArXiv (prépublication)
doi.org
arxiv.org
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling
Yusong Wu
Ethan Manilow
Yi Deng
Rigel Swavely
Kyle Kastner
Tim Cooijmans
Aaron Courville
Anna (Cheng-Zhi) Huang
Jesse Engel
Musical expression requires control of both what notes are played, and how they are performed. Conventional audio synthesizers provide detai… (voir plus)led expressive controls, but at the cost of realism. Black-box neural audio synthesis and concatenative samplers can produce realistic audio, but have few mechanisms for control. In this work, we introduce MIDI-DDSP a hierarchical model of musical instruments that enables both realistic neural audio synthesis and detailed user control. Starting from interpretable Differentiable Digital Signal Processing (DDSP) synthesis parameters, we infer musical notes and high-level properties of their expressive performance (such as timbre, vibrato, dynamics, and articulation). This creates a 3-level hierarchy (notes, performance, synthesis) that affords individuals the option to intervene at each level, or utilize trained priors (performance given notes, synthesis given performance) for creative assistance. Through quantitative experiments and listening tests, we demonstrate that this hierarchy can reconstruct high-fidelity audio, accurately predict performance attributes for a note sequence, independently manipulate the attributes of a given performance, and as a complete system, generate realistic audio from a novel note sequence. By utilizing an interpretable hierarchy, with multiple levels of granularity, MIDI-DDSP opens the door to assistive tools to empower individuals across a diverse range of musical experience.
2022-01-28
ICLR.cc/2022/Conference (présentation orale)
openreview.net
openreview.net
Representation Mixing for TTS Synthesis
Kyle Kastner
Joao Felipe Santos
Yoshua Bengio
Aaron Courville
Recent character and phoneme-based parametric TTS systems using deep learning have shown strong performance in natural speech generation. Ho… (voir plus)wever, the choice between character or phoneme input can create serious limitations for practical deployment, as direct control of pronunciation is crucial in certain cases. We demonstrate a simple method for combining multiple types of linguistic information in a single encoder, named representation mixing, enabling flexible choice between character, phoneme, or mixed representations during inference. Experiments and user studies on a public audiobook corpus show the efficacy of our approach.
2019-05-12
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)
doi.org
arxiv.org
Planning in Dynamic Environments with Conditional Autoregressive Models
Johanna Hansen
Kyle Kastner
Aaron Courville
Gregory Dudek
We demonstrate the use of conditional autoregressive generative models (van den Oord et al., 2016a) over a discrete latent space (van den Oo… (voir plus)rd et al., 2017b) for forward planning with MCTS. In order to test this method, we introduce a new environment featuring varying difficulty levels, along with moving goals and obstacles. The combination of high-quality frame generation and classical planning approaches nearly matches true environment performance for our task, demonstrating the usefulness of this method for model-based planning in dynamic environments.
2018-11-25
ArXiv (prépublication)
arxiv.org
arxiv.org
Harmonic Recomposition using Conditional Autoregressive Modeling
Kyle Kastner
Rithesh Kumar
Tim Cooijmans
Aaron Courville
We demonstrate a conditional autoregressive pipeline for efficient music recomposition, based on methods presented in van den Oord et al.(20… (voir plus)17). Recomposition (Casal & Casey, 2010) focuses on reworking existing musical pieces, adhering to structure at a high level while also re-imagining other aspects of the work. This can involve reuse of pre-existing themes or parts of the original piece, while also requiring the flexibility to generate new content at different levels of granularity. Applying the aforementioned modeling pipeline to recomposition, we show diverse and structured generation conditioned on chord sequence annotations.
2018-11-18
ArXiv (prépublication)
arxiv.org
arxiv.org
Blindfold Baselines for Embodied QA
Ankesh Anand
Eugene Belilovsky
Kyle Kastner
Hugo Larochelle
Aaron Courville
We explore blindfold (question-only) baselines for Embodied Question Answering. The EmbodiedQA task requires an agent to answer a question b… (voir plus)y intelligently navigating in a simulated environment, gathering necessary visual information only through first-person vision before finally answering. Consequently, a blindfold baseline which ignores the environment and visual information is a degenerate solution, yet we show through our experiments on the EQAv1 dataset that a simple question-only baseline achieves state-of-the-art results on the EmbodiedQA task in all cases except when the agent is spawned extremely close to the object.
2018-11-12
ArXiv (prépublication)
arxiv.org
arxiv.org
Char2Wav: End-to-End Speech Synthesis
Jose Sotelo
Soroush Mehri
Kundan Kumar
Joao Felipe Santos
Kyle Kastner
Aaron Courville
Yoshua Bengio
We present Char2Wav, an end-to-end model for speech synthesis. Char2Wav has two components: a reader and a neural vocoder . The reader is an… (voir plus) encoder-decoder model with attention. The encoder is a bidirectional recurrent neural network that accepts text or phonemes as inputs, while the decoder is a recurrent neural network (RNN) with attention that produces vocoder acoustic features. Neural vocoder refers to a conditional extension of SampleRNN which generates raw waveform samples from intermediate representations. Unlike traditional models for speech synthesis, Char2Wav learns to produce audio directly from text
2017-02-17
International Conference on Learning Representations (publié)
openreview.net
openreview.net