Search Results for author:
Found 16 papers, 10 papers with code
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.
The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling
We call this system the Chamber Ensemble Generator (CEG), and use it to generate a large dataset of chorales from four different chamber ensembles (CocoChorales).
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.
Multi-instrument Music Synthesis with Spectrogram Diffusion
An ideal music synthesizer should be both interactive and expressive, generating high-fidelity audio in realtime for arbitrary combinations of instruments and notes.
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling
Musical expression requires control of both what notes are played, and how they are performed.
MT3: Multi-Task Multitrack Music Transcription
Automatic Music Transcription (AMT), inferring musical notes from raw audio, is a challenging task at the core of music understanding.
Ranked #2 on
Music Transcription
on Slakh2100
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
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.
Deep Learning Tools for Audacity: Helping Researchers Expand the Artist's Toolkit
We present a software framework that integrates neural networks into the popular open-source audio editing software, Audacity, with a minimal amount of developer effort.
Sequence-to-Sequence Piano Transcription with Transformers
Automatic Music Transcription has seen significant progress in recent years by training custom deep neural networks on large datasets.
Leveraging Hierarchical Structures for Few-Shot Musical Instrument Recognition
Deep learning work on musical instrument recognition has generally focused on instrument classes for which we have abundant data.
Bespoke Neural Networks for Score-Informed Source Separation
In this paper, we introduce a simple method that can separate arbitrary musical instruments from an audio mixture.
Towards Musically Meaningful Explanations Using Source Separation
Prior work on explainable models in MIR has generally used image processing tools to produce explanations for DNN predictions, but these are not necessarily musically meaningful, or can be listened to (which, arguably, is important in music).
audioLIME: Listenable Explanations Using Source Separation
Deep neural networks (DNNs) are successfully applied in a wide variety of music information retrieval (MIR) tasks but their predictions are usually not interpretable.
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.
WHAM!: Extending Speech Separation to Noisy Environments
Recent progress in separating the speech signals from multiple overlapping speakers using a single audio channel has brought us closer to solving the cocktail party problem.
Ranked #15 on
Speech Separation
on WHAMR!
