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Found 13 papers, 5 papers with code
AudioMorphix: Training-free audio editing with diffusion probabilistic models
Editing sound with precision is a crucial yet underexplored challenge in audio content creation.
From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems
In this paper, we use music generation as a case study to investigate the gap between automatic evaluation metrics and human preferences.
Hierarchical Symbolic Pop Music Generation with Graph Neural Networks
Music is inherently made up of complex structures, and representing them as graphs helps to capture multiple levels of relationships.
Bridging Paintings and Music -- Exploring Emotion based Music Generation through Paintings
Rapid advancements in artificial intelligence have significantly enhanced generative tasks involving music and images, employing both unimodal and multimodal approaches.
From Audio Encoders to Piano Judges: Benchmarking Performance Understanding for Solo Piano
Our study investigates an approach for understanding musical performances through the lens of audio encoding models, focusing on the domain of solo Western classical piano music.
DExter: Learning and Controlling Performance Expression with Diffusion Models
The perceptual-feature-conditioned generation and transferring capabilities of DExter are verified by a proxy model predicting perceptual characteristics of differently steered performances.
Mind the Domain Gap: a Systematic Analysis on Bioacoustic Sound Event Detection
A recent development in the field is the introduction of the task known as few-shot bioacoustic sound event detection, which aims to train a versatile animal sound detector using only a small set of audio samples.
WavCraft: Audio Editing and Generation with Large Language Models
We introduce WavCraft, a collective system that leverages large language models (LLMs) to connect diverse task-specific models for audio content creation and editing.
Acoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities
In this work, we introduce Acoustic Prompt Tuning (APT), a new adapter extending LLMs and VLMs to the audio domain by injecting audio embeddings to the input of LLMs, namely soft prompting.
WavJourney: Compositional Audio Creation with Large Language Models
Subjective evaluations demonstrate the potential of WavJourney in crafting engaging storytelling audio content from text.
Adapting Language-Audio Models as Few-Shot Audio Learners
We presented the Treff adapter, a training-efficient adapter for CLAP, to boost zero-shot classification performance by making use of a small set of labelled data.
Leveraging Pre-trained AudioLDM for Sound Generation: A Benchmark Study
Deep neural networks have recently achieved breakthroughs in sound generation.
Channel Compression: Rethinking Information Redundancy among Channels in CNN Architecture
Aiming at channel compression, a novel convolutional construction named compact convolution is proposed to embrace the progress in spatial convolution, channel grouping and pooling operation.
