# Sound Context Classification Basing on Join Learning Model and Multi-Spectrogram Features

arXiv: https://arxiv.org/abs/2005.12779 · HTML: https://mlsanity.com/paper/2005.12779/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 5 of 5)

- [Wider or Deeper Neural Network Architecture for Acoustic Scene Classification with Mismatched Recording Devices](https://mlsanity.com/paper/2203.12314/) (2022)
- [An Audio-Visual Dataset and Deep Learning Frameworks for Crowded Scene Classification](https://mlsanity.com/paper/2112.09172/) (2021)
- [An Analysis of State-of-the-art Activation Functions For Supervised Deep Neural Network](https://mlsanity.com/paper/2104.02523/) (2021)
- [Low-complexity deep learning frameworks for acoustic scene classification](https://mlsanity.com/paper/2206.06057/) (2022)
- [Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms](https://mlsanity.com/paper/2305.09463/) (2023)

## Abstract (arXiv)

In this paper, we present a deep learning framework applied for Acoustic Scene Classification (ASC), the task of classifying scene contexts from environmental input sounds. An ASC system generally comprises of two main steps, referred to as front-end feature extraction and back-end classification. In the first step, an extractor is used to extract low-level features from raw audio signals. Next, the discriminative features extracted are fed into and classified by a classifier, reporting accuracy results. Aim to develop a robust framework applied for ASC, we address exited issues of both the front-end and back-end components in an ASC system, thus present three main contributions: Firstly, we carry out a comprehensive analysis of spectrogram representation extracted from sound scene input, thus propose the best multi-spectrogram combinations. In terms of back-end classification, we propose a novel join learning architecture using parallel convolutional recurrent networks, which is effective to learn spatial features and temporal sequences of spectrogram input. Finally, good experimental results obtained over benchmark datasets of IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 Task 1, 2017 Task 1, 2018 Task 1A & 1B, LITIS Rouen prove our proposed framework general and robust for ASC task.