HumMusQA: A Human-written Music Understanding QA Benchmark Dataset
HumMusQA: A Human-written Music Understanding QA Benchmark Dataset explores A new benchmark dataset for evaluating Large Audio-Language Models' music understanding capabilities.. Commercial viability score: 5/10 in Audio Understanding.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisMUGEN: Evaluating and Improving Multi-audio Understanding of Large Audio-Language ModelsViability: 4.0
- Builds On ThisMembership Inference Attacks against Large Audio Language ModelsViability: 4.0
- Prior WorkIncompeBench: A Permissively Licensed, Fine-Grained Benchmark for Music Information RetrievalViability: 5.0
- Prior WorkAudioCapBench: Quick Evaluation on Audio Captioning across Sound, Music, and SpeechViability: 5.0
- Prior WorkBeyond Code Snippets: Benchmarking LLMs on Repository-Level Question Answering