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Found 19 papers, 14 papers with code
Anticipatory Music Transformer
We achieve this by interleaving sequences of events and controls, such that controls appear following stopping times in the event sequence.
V2Meow: Meowing to the Visual Beat via Music Generation
Generating high quality music that complements the visual content of a video is a challenging task.
SingSong: Generating musical accompaniments from singing
We present SingSong, a system that generates instrumental music to accompany input vocals, potentially offering musicians and non-musicians alike an intuitive new way to create music featuring their own voice.
Melody transcription via generative pre-training
The combination of generative pre-training and a new dataset for this task results in $77$% stronger performance on melody transcription relative to the strongest available baseline.
It's Raw! Audio Generation with State-Space Models
SaShiMi yields state-of-the-art performance for unconditional waveform generation in the autoregressive setting.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Towards Automatic Instrumentation by Learning to Separate Parts in Symbolic Multitrack Music
In this paper, we aim to further extend this idea and examine the feasibility of automatic instrumentation -- dynamically assigning instruments to notes in solo music during performance.
Codified audio language modeling learns useful representations for music information retrieval
Relative to representations from conventional MIR models which are pre-trained on tagging, we find that using representations from Jukebox as input features yields 30% stronger performance on average across four MIR tasks: tagging, genre classification, emotion recognition, and key detection.
Ranked #1 on
Emotion Recognition
on Emomusic
Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Quality
We release a new benchmark for lexical substitution, the task of finding appropriate substitutes for a target word in a context.
Enabling Language Models to Fill in the Blanks
We show that this approach, which we call infilling by language modeling, can enable LMs to infill entire sentences effectively on three different domains: short stories, scientific abstracts, and lyrics.
LakhNES: Improving multi-instrumental music generation with cross-domain pre-training
We are interested in the task of generating multi-instrumental music scores.
Expediting TTS Synthesis with Adversarial Vocoding
Recent approaches in text-to-speech (TTS) synthesis employ neural network strategies to vocode perceptually-informed spectrogram representations directly into listenable waveforms.
GANSynth: Adversarial Neural Audio Synthesis
Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence.
Piano Genie
We present Piano Genie, an intelligent controller which allows non-musicians to improvise on the piano.
The NES Music Database: A multi-instrumental dataset with expressive performance attributes
Existing research on music generation focuses on composition, but often ignores the expressive performance characteristics required for plausible renditions of resultant pieces.
Adversarial Audio Synthesis
Audio signals are sampled at high temporal resolutions, and learning to synthesize audio requires capturing structure across a range of timescales.
Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition
We investigate the effectiveness of generative adversarial networks (GANs) for speech enhancement, in the context of improving noise robustness of automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Semantically Decomposing the Latent Spaces of Generative Adversarial Networks
Corresponding samples from the real dataset consist of two distinct photographs of the same subject.
Dance Dance Convolution
For the step placement task, we combine recurrent and convolutional neural networks to ingest spectrograms of low-level audio features to predict steps, conditioned on chart difficulty.
