pith. sign in

arxiv: 2311.10057 · v3 · pith:CCQNRIXX · submitted 2023-11-16 · cs.SD · cs.AI· cs.CL· eess.AS

The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation

pith:CCQNRIXXopen to challenge →

classification cs.SD cs.AIcs.CLeess.AS
keywords datasetevaluationmusic-and-languagecorpusdescribermodelsmusicsong
0
0 comments X
read the original abstract

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. The dataset consists of 1.1k human-written natural language descriptions of 706 music recordings, all publicly accessible and released under Creative Common licenses. To showcase the use of our dataset, we benchmark popular models on three key music-and-language tasks (music captioning, text-to-music generation and music-language retrieval). Our experiments highlight the importance of cross-dataset evaluation and offer insights into how researchers can use SDD to gain a broader understanding of model performance.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Live Music Diffusion Models: Efficient Fine-Tuning and Post-Training of Interactive Diffusion Music Generators

    cs.SD 2026-05 unverdicted novelty 7.0

    Live Music Diffusion Models adapt bidirectional diffusion for interactive music generation via KV caching and ARC-Forcing, recovering and exceeding discrete autoregressive efficiency while enabling post-training align...

  2. Modeling Music as a Time-Frequency Image: A 2D Tokenizer for Music Generation

    cs.SD 2026-05 unverdicted novelty 7.0

    BandTok tokenizes Mel-spectrograms as independent time-frequency band tokens from a single codebook and pairs it with 2D RoPE in an autoregressive model to improve music generation over residual multi-codebook tokenizers.

  3. The World is Not Mono: Enabling Spatial Understanding in Large Audio-Language Models

    cs.SD 2026-01 unverdicted novelty 7.0

    TWNM framework equips audio-language models with spatial scene analysis via FOA simulation and metadata-grounded training, reaching 70.8% accuracy on a new ASA benchmark.

  4. MECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks

    eess.AS 2025-07 unverdicted novelty 7.0

    MECAT is a multi-expert benchmark for audio AI offering fine-grained captions and QA pairs generated via expert models and LLM reasoning, paired with the DATE metric that combines semantic similarity and cross-sample ...

  5. Real-Time Interactive Music Generation via Data-Free Streaming Consistency Distillation

    cs.SD 2026-06 unverdicted novelty 6.0

    A data-free streaming consistency distillation framework enables single-step autoregressive generation from text-to-music models for real-time interactive use while preserving timbre and rhythm via latent, spectral, a...

  6. STAR-VAE: Structured Topology-Aware Regularization for Audio Reconstruction and Generation

    eess.AS 2026-06 unverdicted novelty 4.0

    STAR-VAE introduces topology-aware regularization to reshape VAE latent geometry for audio, claiming to resolve the Rate-Distortion-Regularity Trilemma and achieve SOTA reconstruction.

  7. A Survey of Advancing Audio Super-Resolution and Bandwidth Extension from Discriminative to Generative Models

    eess.AS 2026-05 unverdicted novelty 2.0

    A structured survey of audio bandwidth extension that organizes the transition from deterministic discriminative DNNs to generative approaches including GANs, diffusion models, and flow-based methods.