# Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms

arXiv: https://arxiv.org/abs/1703.01789 · HTML: https://mlsanity.com/paper/1703.01789/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2017.

## Built on by (most cited first, 27 of 27)

- [VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text](https://mlsanity.com/paper/2104.11178/) (2021)
- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networks](https://mlsanity.com/paper/2110.08858/) (2021)
- [MuseChat: A Conversational Music Recommendation System for Videos](https://mlsanity.com/paper/2310.06282/) (2023)
- [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)
- [Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture](https://mlsanity.com/paper/2105.13854/) (2021)
- [Contrastive Learning with Positive-Negative Frame Mask for Music Representation](https://mlsanity.com/paper/2203.09129/) (2022)
- [Investigating Multi-Feature Selection and Ensembling for Audio Classification](https://mlsanity.com/paper/2206.07511/) (2022)
- [VPN: Video Provenance Network for Robust Content Attribution](https://mlsanity.com/paper/2109.10038/) (2021)
- [LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging](https://mlsanity.com/paper/2409.11264/) (2024)
- [A Spectral Enabled GAN for Time Series Data Generation](https://mlsanity.com/paper/2103.01904/) (2021)
- [Jointist: Joint Learning for Multi-instrument Transcription and Its Applications](https://mlsanity.com/paper/2206.10805/) (2022)
- [Representation Learning for Appliance Recognition: A Comparison to Classical Machine Learning](https://mlsanity.com/paper/2209.03759/) (2022)
- [Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training](https://mlsanity.com/paper/2302.00286/) (2023)
- [AudRandAug: Random Image Augmentations for Audio Classification](https://mlsanity.com/paper/2309.04762/) (2023)
- [Universal Music Representations? Evaluating Foundation Models on World Music Corpora](https://mlsanity.com/paper/2506.17055/) (2025)
- [Emergent musical properties of a transformer under contrastive self-supervised learning](https://mlsanity.com/paper/2506.23873/) (2025)
- [Music Auto-Tagging with Robust Music Representation Learned via Domain Adversarial Training](https://mlsanity.com/paper/2401.15323/) (2024)
- [Music auto-tagging in the long tail: A few-shot approach](https://mlsanity.com/paper/2409.07730/) (2024)
- [End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network](https://mlsanity.com/paper/2204.11479/) (2022)
- [Improving Primate Sounds Classification using Binary Presorting for Deep Learning](https://mlsanity.com/paper/2306.16054/) (2023)
- [An approach to hummed-tune and song sequences matching](https://mlsanity.com/paper/2410.20352/) (2024)
- [Semantic-Aware Interpretable Multimodal Music Auto-Tagging](https://mlsanity.com/paper/2505.17233/) (2025)
- [Spectrogram features for audio and speech analysis](https://mlsanity.com/paper/2603.14917/) (2026)
- [CoJEPA: Combining Contrastive Learning and JEPA for Global-Local Music Representations](https://mlsanity.com/paper/2608.30974/) (2026)

## Abstract (arXiv)

Recently, the end-to-end approach that learns hierarchical representations from raw data using deep convolutional neural networks has been successfully explored in the image, text and speech domains. This approach was applied to musical signals as well but has been not fully explored yet. To this end, we propose sample-level deep convolutional neural networks which learn representations from very small grains of waveforms (e.g. 2 or 3 samples) beyond typical frame-level input representations. Our experiments show how deep architectures with sample-level filters improve the accuracy in music auto-tagging and they provide results comparable to previous state-of-the-art performances for the Magnatagatune dataset and Million Song Dataset. In addition, we visualize filters learned in a sample-level DCNN in each layer to identify hierarchically learned features and show that they are sensitive to log-scaled frequency along layer, such as mel-frequency spectrogram that is widely used in music classification systems.