# Learning Environmental Sounds with Multi-scale Convolutional Neural Network

arXiv: https://arxiv.org/abs/1803.10219 · HTML: https://mlsanity.com/paper/1803.10219/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification](https://mlsanity.com/paper/2103.01929/) (2021)
- [A Universal Model for Cross Modality Mapping by Relational Reasoning](https://mlsanity.com/paper/2102.13360/) (2021)
- [Study of positional encoding approaches for Audio Spectrogram Transformers](https://mlsanity.com/paper/2110.06999/) (2021)
- [Time-Frequency Localization Using Deep Convolutional Maxout Neural Network in Persian Speech Recognition](https://mlsanity.com/paper/2108.03818/) (2021)

## Abstract (arXiv)

Deep learning has dramatically improved the performance of sounds recognition. However, learning acoustic models directly from the raw waveform is still challenging. Current waveform-based models generally use time-domain convolutional layers to extract features. The features extracted by single size filters are insufficient for building discriminative representation of audios. In this paper, we propose multi-scale convolution operation, which can get better audio representation by improving the frequency resolution and learning filters cross all frequency area. For leveraging the waveform-based features and spectrogram-based features in a single model, we introduce two-phase method to fuse the different features. Finally, we propose a novel end-to-end network called WaveMsNet based on the multi-scale convolution operation and two-phase method. On the environmental sounds classification datasets ESC-10 and ESC-50, the classification accuracies of our WaveMsNet achieve 93.75% and 79.10% respectively, which improve significantly from the previous methods.