Multi-Temporal Resolution Convolutional Neural Networks for Acoustic Scene Classification

Abstract

In this paper we present a Deep Neural Network architecture for the task of acoustic scene classification which harnesses information from increasing temporal resolutions of Mel-Spectrogram segments. This architecture is composed of separated parallel Convolutional Neural Networks which learn spectral and temporal representations for each input resolution. The resolutions are chosen to cover fine-grained characteristics of a scene's spectral texture as well as its distribution of acoustic events. The proposed model shows a 3.56% absolute improvement of the best performing single resolution model and 12.49% of the DCASE 2017 Acoustic Scenes Classification task baseline.

arXiv commentIn Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), November 2017

References in corpus (2)

• Deep Convolutional Neural Networks and Data Augmentation for Acoustic Event Detection
• Convolutional Gated Recurrent Neural Network Incorporating Spatial Features for Audio Tagging

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