# An Audio-Based Deep Learning Framework For BBC Television Programme Classification

arXiv: https://arxiv.org/abs/2104.01161 · HTML: https://mlsanity.com/paper/2104.01161/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2021.

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

- [Classification of Spontaneous and Scripted Speech for Multilingual Audio](https://mlsanity.com/paper/2412.11896/) (2024)
- [A Toolchain for Comprehensive Audio/Video Analysis Using Deep Learning Based Multimodal Approach (A use case of riot or violent context detection)](https://mlsanity.com/paper/2407.03110/) (2024)

## Abstract (arXiv)

This paper proposes a deep learning framework for classification of BBC television programmes using audio. The audio is firstly transformed into spectrograms, which are fed into a pre-trained convolutional Neural Network (CNN), obtaining predicted probabilities of sound events occurring in the audio recording. Statistics for the predicted probabilities and detected sound events are then calculated to extract discriminative features representing the television programmes. Finally, the embedded features extracted are fed into a classifier for classifying the programmes into different genres. Our experiments are conducted over a dataset of 6,160 programmes belonging to nine genres labelled by the BBC. We achieve an average classification accuracy of 93.7% over 14-fold cross validation. This demonstrates the efficacy of the proposed framework for the task of audio-based classification of television programmes.