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Found 19 papers, 9 papers with code
VampNet: Music Generation via Masked Acoustic Token Modeling
We introduce VampNet, a masked acoustic token modeling approach to music synthesis, compression, inpainting, and variation.
High-Fidelity Audio Compression with Improved RVQGAN
Language models have been successfully used to model natural signals, such as images, speech, and music.
Music Separation Enhancement with Generative Modeling
Despite phenomenal progress in recent years, state-of-the-art music separation systems produce source estimates with significant perceptual shortcomings, such as adding extraneous noise or removing harmonics.
Unsupervised Source Separation By Steering Pretrained Music Models
We showcase an unsupervised method that repurposes deep models trained for music generation and music tagging for audio source separation, without any retraining.
Wav2CLIP: Learning Robust Audio Representations From CLIP
We propose Wav2CLIP, a robust audio representation learning method by distilling from Contrastive Language-Image Pre-training (CLIP).
Chunked Autoregressive GAN for Conditional Waveform Synthesis
We show that simple pitch and periodicity conditioning is insufficient for reducing this error relative to using autoregression.
What's All the FUSS About Free Universal Sound Separation Data?
We introduce the Free Universal Sound Separation (FUSS) dataset, a new corpus for experiments in separating mixtures of an unknown number of sounds from an open domain of sound types.
A Study of Transfer Learning in Music Source Separation
Supervised deep learning methods for performing audio source separation can be very effective in domains where there is a large amount of training data.
AutoClip: Adaptive Gradient Clipping for Source Separation Networks
Clipping the gradient is a known approach to improving gradient descent, but requires hand selection of a clipping threshold hyperparameter.
OtoWorld: Towards Learning to Separate by Learning to Move
The agent receives a reward for turning off a source.
Bach or Mock? A Grading Function for Chorales in the Style of J.S. Bach
Deep generative systems that learn probabilistic models from a corpus of existing music do not explicitly encode knowledge of a musical style, compared to traditional rule-based systems.
Incorporating Music Knowledge in Continual Dataset Augmentation for Music Generation
In this paper, we present augmentative generation (Aug-Gen), a method of dataset augmentation for any music generation system trained on a resource-constrained domain.
Model selection for deep audio source separation via clustering analysis
We compare our confidence-based ensemble approach to using individual models with no selection, to an oracle that always selects the best model and to a random model selector.
Bootstrapping deep music separation from primitive auditory grouping principles
They are trained on synthetic mixtures of audio made from isolated sound source recordings so that ground truth for the separation is known.
Simultaneous Separation and Transcription of Mixtures with Multiple Polyphonic and Percussive Instruments
We present a single deep learning architecture that can both separate an audio recording of a musical mixture into constituent single-instrument recordings and transcribe these instruments into a human-readable format at the same time, learning a shared musical representation for both tasks.
Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity
In this paper, we present the synthesized Lakh dataset (Slakh) as a new tool for music source separation research.
Class-conditional embeddings for music source separation
Isolating individual instruments in a musical mixture has a myriad of potential applications, and seems imminently achievable given the levels of performance reached by recent deep learning methods.
Bootstrapping single-channel source separation via unsupervised spatial clustering on stereo mixtures
These estimates, together with a weighting scheme in the time-frequency domain, based on confidence in the separation quality, are used to train a deep learning model that can be used for single-channel separation, where no source direction information is available.
VocalSet: A Singing Voice Dataset
We present VocalSet, a singing voice dataset of a capella singing.
