# CoLLAP: Contrastive Long-form Language-Audio Pretraining with Musical Temporal Structure Augmentation

arXiv: https://arxiv.org/abs/2410.02271 · HTML: https://mlsanity.com/paper/2410.02271/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [BEATs: Audio Pre-Training with Acoustic Tokenizers](https://mlsanity.com/paper/2212.09058/) (2022)
- [LP-MusicCaps: LLM-Based Pseudo Music Captioning](https://mlsanity.com/paper/2307.16372/) (2023)
- [The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation](https://mlsanity.com/paper/2311.10057/) (2023)
- [Cacophony: An Improved Contrastive Audio-Text Model](https://mlsanity.com/paper/2402.06986/) (2024)
- [T-CLAP: Temporal-Enhanced Contrastive Language-Audio Pretraining](https://mlsanity.com/paper/2404.17806/) (2024)
- [Futga: Towards Fine-grained Music Understanding through Temporally-enhanced Generative Augmentation](https://mlsanity.com/paper/2407.20445/) (2024)

## Built on by (most cited first, 4 of 4)

- [GLAP: General contrastive audio-text pretraining across domains and languages](https://mlsanity.com/paper/2506.11350/) (2025)
- [GD-Retriever: Controllable Generative Text-Music Retrieval with Diffusion Models](https://mlsanity.com/paper/2506.17886/) (2025)
- [Membership Inference for Contrastive Pre-training Models with Text-only PII Queries](https://mlsanity.com/paper/2603.14222/) (2026)
- [Local Multimodal Music Alignment from Global Supervision](https://mlsanity.com/paper/2607.10023/) (2026)

## Abstract (arXiv)

Modeling temporal characteristics plays a significant role in the representation learning of audio waveform. We propose Contrastive Long-form Language-Audio Pretraining (CoLLAP) to significantly extend the perception window for both the input audio (up to 5 minutes) and the language descriptions (exceeding 250 words), while enabling contrastive learning across modalities and temporal dynamics. Leveraging recent Music-LLMs to generate long-form music captions for full-length songs, augmented with musical temporal structures, we collect 51.3K audio-text pairs derived from the large-scale AudioSet training dataset, where the average audio length reaches 288 seconds. We propose a novel contrastive learning architecture that fuses language representations with structured audio representations by segmenting each song into clips and extracting their embeddings. With an attention mechanism, we capture multimodal temporal correlations, allowing the model to automatically weigh and enhance the final fusion score for improved contrastive alignment. Finally, we develop two variants of the CoLLAP model with different types of backbone language models. Through comprehensive experiments on multiple long-form music-text retrieval datasets, we demonstrate consistent performance improvement in retrieval accuracy compared with baselines. We also show the pretrained CoLLAP models can be transferred to various music information retrieval tasks, with heterogeneous long-form multimodal contexts.