# Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms

arXiv: https://arxiv.org/abs/1710.10451 · HTML: https://mlsanity.com/paper/1710.10451/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2017.

## Built on by (most cited first, 13 of 13)

- [MusCaps: Generating Captions for Music Audio](https://mlsanity.com/paper/2104.11984/) (2021)
- [Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition](https://mlsanity.com/paper/2104.06517/) (2021)
- [LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging](https://mlsanity.com/paper/2409.11264/) (2024)
- [Audio Embeddings as Teachers for Music Classification](https://mlsanity.com/paper/2306.17424/) (2023)
- [Leveraging Pre-Trained Autoencoders for Interpretable Prototype Learning of Music Audio](https://mlsanity.com/paper/2402.09318/) (2024)
- [Universal Music Representations? Evaluating Foundation Models on World Music Corpora](https://mlsanity.com/paper/2506.17055/) (2025)
- [MS-SincResNet: Joint learning of 1D and 2D kernels using multi-scale SincNet and ResNet for music genre classification](https://mlsanity.com/paper/2109.08910/) (2021)
- [Music Auto-Tagging with Robust Music Representation Learned via Domain Adversarial Training](https://mlsanity.com/paper/2401.15323/) (2024)
- [MuSLCAT: Multi-Scale Multi-Level Convolutional Attention Transformer for Discriminative Music Modeling on Raw Waveforms](https://mlsanity.com/paper/2104.02309/) (2021)
- [Learning Sparse Analytic Filters for Piano Transcription](https://mlsanity.com/paper/2108.10382/) (2021)
- [Discussion of Features for Acoustic Anomaly Detection under Industrial Disturbing Noise in an End-of-Line Test of Geared Motors](https://mlsanity.com/paper/2211.01716/) (2022)
- [FolkTalent: Enhancing Classification and Tagging of Indian Folk Paintings](https://mlsanity.com/paper/2405.08776/) (2024)
- [Learning Music Audio Representations With Limited Data](https://mlsanity.com/paper/2505.06042/) (2025)

## Abstract (arXiv)

Recent work has shown that the end-to-end approach using convolutional neural network (CNN) is effective in various types of machine learning tasks. For audio signals, the approach takes raw waveforms as input using an 1-D convolution layer. In this paper, we improve the 1-D CNN architecture for music auto-tagging by adopting building blocks from state-of-the-art image classification models, ResNets and SENets, and adding multi-level feature aggregation to it. We compare different combinations of the modules in building CNN architectures. The results show that they achieve significant improvements over previous state-of-the-art models on the MagnaTagATune dataset and comparable results on Million Song Dataset. Furthermore, we analyze and visualize our model to show how the 1-D CNN operates.