# Automatic tagging using deep convolutional neural networks

arXiv: https://arxiv.org/abs/1606.00298 · HTML: https://mlsanity.com/paper/1606.00298/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2016.

## Built on by (most cited first, 30 of 40)

- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead](https://mlsanity.com/paper/2105.09121/) (2021)
- [MusCaps: Generating Captions for Music Audio](https://mlsanity.com/paper/2104.11984/) (2021)
- [MuseChat: A Conversational Music Recommendation System for Videos](https://mlsanity.com/paper/2310.06282/) (2023)
- [Multi-Task Self-Supervised Pre-Training for Music Classification](https://mlsanity.com/paper/2102.03229/) (2021)
- [Language-Guided Music Recommendation for Video via Prompt Analogies](https://mlsanity.com/paper/2306.09327/) (2023)
- [SpecTNT: a Time-Frequency Transformer for Music Audio](https://mlsanity.com/paper/2110.09127/) (2021)
- [Continual Transformers: Redundancy-Free Attention for Online Inference](https://mlsanity.com/paper/2201.06268/) (2022)
- [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)
- [Learning Music Sequence Representation from Text Supervision](https://mlsanity.com/paper/2305.19602/) (2023)
- [Audio Embeddings as Teachers for Music Classification](https://mlsanity.com/paper/2306.17424/) (2023)
- [From West to East: Who can understand the music of the others better?](https://mlsanity.com/paper/2307.09795/) (2023)
- [Joint Music and Language Attention Models for Zero-shot Music Tagging](https://mlsanity.com/paper/2310.10159/) (2023)
- [Pretrained audio neural networks for Speech emotion recognition in Portuguese](https://mlsanity.com/paper/2210.14716/) (2022)
- [LEAN: Light and Efficient Audio Classification Network](https://mlsanity.com/paper/2305.12712/) (2023)
- [MERGE -- A Bimodal Audio-Lyrics Dataset for Static Music Emotion Recognition](https://mlsanity.com/paper/2407.06060/) (2024)
- [Listen, Read, and Identify: Multimodal Singing Language Identification of Music](https://mlsanity.com/paper/2103.01893/) (2021)
- [Employing Crowdsourcing for Enriching a Music Knowledge Base in Higher Education](https://mlsanity.com/paper/2306.07310/) (2023)
- [Movie Trailer Genre Classification Using Multimodal Pretrained Features](https://mlsanity.com/paper/2410.19760/) (2024)
- [Continual Low-Rank Scaled Dot-product Attention](https://mlsanity.com/paper/2412.03214/) (2024)
- [A Visual Domain Transfer Learning Approach for Heartbeat Sound Classification](https://mlsanity.com/paper/2107.13237/) (2021)
- [Unsupervised Source Separation By Steering Pretrained Music Models](https://mlsanity.com/paper/2110.13071/) (2021)
- [Towards Contrastive Learning in Music Video Domain](https://mlsanity.com/paper/2309.00347/) (2023)
- [MDSC: Towards Evaluating the Style Consistency Between Music and Dance](https://mlsanity.com/paper/2309.01340/) (2023)
- [Automated Bioacoustic Monitoring for South African Bird Species on Unlabeled Data](https://mlsanity.com/paper/2406.13579/) (2024)
- [Knowledge Distillation for Real-Time Classification of Early Media in Voice Communications](https://mlsanity.com/paper/2410.21478/) (2024)
- [MuSLCAT: Multi-Scale Multi-Level Convolutional Attention Transformer for Discriminative Music Modeling on Raw Waveforms](https://mlsanity.com/paper/2104.02309/) (2021)
- [Timbre Classification of Musical Instruments with a Deep Learning Multi-Head Attention-Based Model](https://mlsanity.com/paper/2107.06231/) (2021)
- [Multi-task Learning with Metadata for Music Mood Classification](https://mlsanity.com/paper/2110.04765/) (2021)

## Abstract (arXiv)

We present a content-based automatic music tagging algorithm using fully convolutional neural networks (FCNs). We evaluate different architectures consisting of 2D convolutional layers and subsampling layers only. In the experiments, we measure the AUC-ROC scores of the architectures with different complexities and input types using the MagnaTagATune dataset, where a 4-layer architecture shows state-of-the-art performance with mel-spectrogram input. Furthermore, we evaluated the performances of the architectures with varying the number of layers on a larger dataset (Million Song Dataset), and found that deeper models outperformed the 4-layer architecture. The experiments show that mel-spectrogram is an effective time-frequency representation for automatic tagging and that more complex models benefit from more training data.