# musicnn: Pre-trained convolutional neural networks for music audio tagging

arXiv: https://arxiv.org/abs/1909.06654 · HTML: https://mlsanity.com/paper/1909.06654/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2019.

## Built on by (most cited first, 30 of 50)

- [MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training](https://mlsanity.com/paper/2306.00107/) (2023)
- [Codified audio language modeling learns useful representations for music information retrieval](https://mlsanity.com/paper/2107.05677/) (2021)
- [MARBLE: Music Audio Representation Benchmark for Universal Evaluation](https://mlsanity.com/paper/2306.10548/) (2023)
- [Supervised and Unsupervised Learning of Audio Representations for Music Understanding](https://mlsanity.com/paper/2210.03799/) (2022)
- [Music2Latent: Consistency Autoencoders for Latent Audio Compression](https://mlsanity.com/paper/2408.06500/) (2024)
- [MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning](https://mlsanity.com/paper/2212.02508/) (2022)
- [MuseChat: A Conversational Music Recommendation System for Videos](https://mlsanity.com/paper/2310.06282/) (2023)
- [It's Time for Artistic Correspondence in Music and Video](https://mlsanity.com/paper/2206.07148/) (2022)
- [Language-Guided Music Recommendation for Video via Prompt Analogies](https://mlsanity.com/paper/2306.09327/) (2023)
- [Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming](https://mlsanity.com/paper/2211.01317/) (2022)
- [A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios](https://mlsanity.com/paper/2409.17864/) (2024)
- [Flow Moods: Recommending Music by Moods on Deezer](https://mlsanity.com/paper/2207.11229/) (2022)
- [Contrastive Learning with Positive-Negative Frame Mask for Music Representation](https://mlsanity.com/paper/2203.09129/) (2022)
- [ECC Analyzer: Extract Trading Signal from Earnings Conference Calls using Large Language Model for Stock Performance Prediction](https://mlsanity.com/paper/2404.18470/) (2024)
- [LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging](https://mlsanity.com/paper/2409.11264/) (2024)
- [Jointist: Joint Learning for Multi-instrument Transcription and Its Applications](https://mlsanity.com/paper/2206.10805/) (2022)
- [Music Foundation Model as Generic Booster for Music Downstream Tasks](https://mlsanity.com/paper/2411.01135/) (2024)
- [Towards Proper Contrastive Self-supervised Learning Strategies For Music Audio Representation](https://mlsanity.com/paper/2207.04471/) (2022)
- [Learning Music Sequence Representation from Text Supervision](https://mlsanity.com/paper/2305.19602/) (2023)
- [From West to East: Who can understand the music of the others better?](https://mlsanity.com/paper/2307.09795/) (2023)
- [FilmComposer: LLM-Driven Music Production for Silent Film Clips](https://mlsanity.com/paper/2503.08147/) (2025)
- [Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning](https://mlsanity.com/paper/2207.13909/) (2022)
- [Music2Latent2: Audio Compression with Summary Embeddings and Autoregressive Decoding](https://mlsanity.com/paper/2501.17578/) (2025)
- [A Comprehensive Survey on Generative AI for Video-to-Music Generation](https://mlsanity.com/paper/2502.12489/) (2025)
- [Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features](https://mlsanity.com/paper/2110.08862/) (2021)
- [Learning Unsupervised Hierarchies of Audio Concepts](https://mlsanity.com/paper/2207.11231/) (2022)
- [Music Instrument Classification Reprogrammed](https://mlsanity.com/paper/2211.08379/) (2022)
- [Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training](https://mlsanity.com/paper/2302.00286/) (2023)
- [Myna: Masking-Based Contrastive Learning of Musical Representations](https://mlsanity.com/paper/2502.12511/) (2025)
- [Universal Music Representations? Evaluating Foundation Models on World Music Corpora](https://mlsanity.com/paper/2506.17055/) (2025)

## Abstract (arXiv)

Pronounced as "musician", the musicnn library contains a set of pre-trained musically motivated convolutional neural networks for music audio tagging: https://github.com/jordipons/musicnn. This repository also includes some pre-trained vgg-like baselines. These models can be used as out-of-the-box music audio taggers, as music feature extractors, or as pre-trained models for transfer learning. We also provide the code to train the aforementioned models: https://github.com/jordipons/musicnn-training. This framework also allows implementing novel models. For example, a musically motivated convolutional neural network with an attention-based output layer (instead of the temporal pooling layer) can achieve state-of-the-art results for music audio tagging: 90.77 ROC-AUC / 38.61 PR-AUC on the MagnaTagATune dataset --- and 88.81 ROC-AUC / 31.51 PR-AUC on the Million Song Dataset.