138 citations · 368 across the 28 of their papers we have counts for
8 papers · 1 filter
SongCraft: Unified Song Generation and Editing with Reconstructive Learning
Haohe Liu, Varun Nagaraja, Gael Le Lan +5
Song generation and editing have mostly been treated as separate tasks. Existing editing methods often require noise injection and regeneration or curated paired training data. We…
Lightweight and Generalizable Acoustic Scene Representations via Contrastive Fine-Tuning and Distillation
Kuang Yuan, Yang Gao, Xilin Li +4
Acoustic scene classification (ASC) models on edge devices typically operate under fixed class assumptions, lacking the transferability needed for real-world applications that requ…
First-Shot Unsupervised Anomalous Sound Detection With Unknown Anomalies Estimated by Metadata-Assisted Audio Generation
Hejing Zhang, Qiaoxi Zhu, Jian Guan +6
First-shot (FS) unsupervised anomalous sound detection (ASD) is a brand-new task introduced in DCASE 2023 Challenge Task 2, where the anomalous sounds for the target machine types…
Enhance audio generation controllability through representation similarity regularization
Yangyang Shi, Gael Le Lan, Varun Nagaraja +6
This paper presents an innovative approach to enhance control over audio generation by emphasizing the alignment between audio and text representations during model training. In th…
AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining
Haohe Liu, Yi Yuan, Xubo Liu +7
Although audio generation shares commonalities across different types of audio, such as speech, music, and sound effects, designing models for each type requires careful considerat…
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
Haohe Liu, Zehua Chen, Yi Yuan +5
Text-to-audio (TTA) system has recently gained attention for its ability to synthesize general audio based on text descriptions. However, previous studies in TTA have limited gener…
Automated Audio Captioning via Fusion of Low- and High- Dimensional Features
Jianyuan Sun, Xubo Liu, Xinhao Mei +3
Automated audio captioning (AAC) aims to describe the content of an audio clip using simple sentences. Existing AAC methods are developed based on an encoder-decoder architecture t…
Surrey System for DCASE 2022 Task 5: Few-shot Bioacoustic Event Detection with Segment-level Metric Learning
Haohe Liu, Xubo Liu, Xinhao Mei +3
Few-shot audio event detection is a task that detects the occurrence time of a novel sound class given a few examples. In this work, we propose a system based on segment-level metr…