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Found 11 papers, 5 papers with code
Adaptive Accompaniment with ReaLchords
Jamming requires coordination, anticipation, and collaborative creativity between musicians.
ReaLJam: Real-Time Human-AI Music Jamming with Reinforcement Learning-Tuned Transformers
We enable real-time interactions using the concept of anticipation, where the agent continually predicts how the performance will unfold and visually conveys its plan to the user.
Hierarchical Generative Modeling of Melodic Vocal Contours in Hindustani Classical Music
Hindustani music is a performance-driven oral tradition that exhibits the rendition of rich melodic patterns.
The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation
We introduce the Song Describer dataset (SDD), a new crowdsourced corpus of high-quality audio-caption pairs, designed for the evaluation of music-and-language models.
MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies
Diffusion models have shown promising results in cross-modal generation tasks, including text-to-image and text-to-audio generation.
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).
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Multi-instrument Music Transcription
on Slakh2100
Information Retrieval
Multi-instrument Music Transcription
+2
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.
An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement Learning
Automated audio captioning aims to use natural language to describe the content of audio data.
Peking Opera Synthesis via Duration Informed Attention Network
In this work, we propose to deal with this issue and synthesize expressive Peking Opera singing from the music score based on the Duration Informed Attention Network (DurIAN) framework.
Synthesising Expressiveness in Peking Opera via Duration Informed Attention Network
This paper presents a method that generates expressive singing voice of Peking opera.
Learning Singing From Speech
The proposed algorithm first integrate speech and singing synthesis into a unified framework, and learns universal speaker embeddings that are shareable between speech and singing synthesis tasks.
