# Spectral and Rhythm Features for Audio Classification with Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2410.06927 · HTML: https://mlsanity.com/paper/2410.06927/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 9 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification](https://mlsanity.com/paper/1608.04363/) (2016)
- [Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation](https://mlsanity.com/paper/2211.04772/) (2022)
- [Deep Convolutional Neural Networks and Data Augmentation for Acoustic Event Detection](https://mlsanity.com/paper/1604.07160/) (2016)

## Built on by (most cited first, 2 of 2)

- [15,500 Seconds: Lean UAV Classification Using EfficientNet and Lightweight Fine-Tuning](https://mlsanity.com/paper/2506.11049/) (2025)
- [The Unbearable Weight: Scaling Models and Methods for UAV Audio Classification](https://mlsanity.com/paper/2609.17884/) (2026)

## Abstract (arXiv)

Convolutional neural networks (CNNs) are widely used in computer vision. They can be used not only for conventional digital image material to recognize patterns, but also for feature extraction from digital imagery representing spectral and rhythm features extracted from time-domain digital audio signals for the acoustic classification of sounds. Different spectral and rhythm feature representations like mel-scaled spectrograms, mel-frequency cepstral coefficients (MFCCs), cyclic tempograms, short-time Fourier transform (STFT) chromagrams, constant-Q transform (CQT) chromagrams and chroma energy normalized statistics (CENS) chromagrams are investigated in terms of the audio classification performance using a deep convolutional neural network. It can be clearly shown that the mel-scaled spectrograms and the mel-frequency cepstral coefficients (MFCCs) perform significantly better than the other spectral and rhythm features investigated in this research for audio classification tasks using deep CNNs. The experiments were carried out with the aid of the ESC-50 dataset with 2,000 labeled environmental audio recordings.