3 citations · 4 across the 17 of their papers we have counts for
11 papers · 1 filter
MMAE: A Massive Multitask Audio Editing Benchmark
Ziyang Ma, Ruiqi Yan, Ruiyang Xu +35
We introduce MMAE, a Massive Multitask Audio Editing benchmark, serving as the first comprehensive evaluation testbed designed for general-purpose instruction-based audio editing.…
TinyMU: A Compact Audio-Language Model for Music Understanding
Xiquan Li, Aurian Quelennec, Slim Essid
Music understanding and reasoning are central challenges in the Music Information Research field, with applications ranging from retrieval and recommendation to music agents and vi…
FineLAP: Taming Heterogeneous Supervision for Fine-grained Language-Audio Pretraining
Xiquan Li, Xuenan Xu, Ziyang Ma +4
Contrastively pretrained audio-language models (e.g., CLAP) excel at clip-level understanding but struggle with frame-level tasks. Existing extensions fail to exploit the varying g…
Resonate: Reinforcing Text-to-Audio Generation via Online Feedback from Large Audio Language Models
Xiquan Li, Junxi Liu, Wenxi Chen +3
Reinforcement Learning (RL) has become an effective paradigm for enhancing Large Language Models (LLMs) and visual generative models. However, its application in text-to-audio (TTA…
Audio ControlNet for Fine-Grained Audio Generation and Editing
Haina Zhu, Yao Xiao, Xiquan Li +5
We study the fine-grained text-to-audio (T2A) generation task. While recent models can synthesize high-quality audio from text descriptions, they often lack precise control over at…
SLAM-LLM: A Modular, Open-Source Multimodal Large Language Model Framework and Best Practice for Speech, Language, Audio and Music Processing
Ziyang Ma, Guanrou Yang, Wenxi Chen +19
The recent surge in open-source Multimodal Large Language Models (MLLM) frameworks, such as LLaVA, provides a convenient kickoff for artificial intelligence developers and research…
MeanAudio: Fast and Faithful Text-to-Audio Generation with Mean Flows
Xiquan Li, Junxi Liu, Yuzhe Liang +3
Recent years have witnessed remarkable progress in Text-to-Audio Generation (TTA), providing sound creators with powerful tools to transform inspirations into vivid audio. Yet desp…
Towards Reliable Large Audio Language Model
Ziyang Ma, Xiquan Li, Yakun Song +8
Recent advancements in large audio language models (LALMs) have demonstrated impressive results and promising prospects in universal understanding and reasoning across speech, musi…
MMAR: A Challenging Benchmark for Deep Reasoning in Speech, Audio, Music, and Their Mix
Ziyang Ma, Yinghao Ma, Yanqiao Zhu +31
We introduce MMAR, a new benchmark designed to evaluate the deep reasoning capabilities of Audio-Language Models (ALMs) across massive multi-disciplinary tasks. MMAR comprises 1,00…
DRCap: Decoding CLAP Latents with Retrieval-Augmented Generation for Zero-shot Audio Captioning
Xiquan Li, Wenxi Chen, Ziyang Ma +5
While automated audio captioning (AAC) has made notable progress, traditional fully supervised AAC models still face two critical challenges: the need for expensive audio-text pair…
EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark
Ziyang Ma, Mingjie Chen, Hezhao Zhang +6
Speech emotion recognition (SER) is an important part of human-computer interaction, receiving extensive attention from both industry and academia. However, the current research fi…