# Raw Waveform-based Audio Classification Using Sample-level CNN Architectures

arXiv: https://arxiv.org/abs/1712.00866 · HTML: https://mlsanity.com/paper/1712.00866/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2017.

## Built on by (most cited first, 12 of 12)

- [How to Design a Three-Stage Architecture for Audio-Visual Active Speaker Detection in the Wild](https://mlsanity.com/paper/2106.03932/) (2021)
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- [You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection](https://mlsanity.com/paper/2109.00962/) (2021)
- [Learning Temporal Resolution in Spectrogram for Audio Classification](https://mlsanity.com/paper/2210.01719/) (2022)
- [NAAQA: A Neural Architecture for Acoustic Question Answering](https://mlsanity.com/paper/2106.06147/) (2021)
- [XAI-based Comparison of Input Representations for Audio Event Classification](https://mlsanity.com/paper/2304.14019/) (2023)
- [AudRandAug: Random Image Augmentations for Audio Classification](https://mlsanity.com/paper/2309.04762/) (2023)
- [Artificially Synthesising Data for Audio Classification and Segmentation to Improve Speech and Music Detection in Radio Broadcast](https://mlsanity.com/paper/2102.09959/) (2021)
- [Generative AI-based data augmentation for improved bioacoustic classification in noisy environments](https://mlsanity.com/paper/2412.01530/) (2024)
- [Structural Bottlenecks on Frequency Representation in End-to-End Audio Models](https://mlsanity.com/paper/2607.08545/) (2026)

## Abstract (arXiv)

Music, speech, and acoustic scene sound are often handled separately in the audio domain because of their different signal characteristics. However, as the image domain grows rapidly by versatile image classification models, it is necessary to study extensible classification models in the audio domain as well. In this study, we approach this problem using two types of sample-level deep convolutional neural networks that take raw waveforms as input and uses filters with small granularity. One is a basic model that consists of convolution and pooling layers. The other is an improved model that additionally has residual connections, squeeze-and-excitation modules and multi-level concatenation. We show that the sample-level models reach state-of-the-art performance levels for the three different categories of sound. Also, we visualize the filters along layers and compare the characteristics of learned filters.