Work

Audio Scene Classification with Deep Recurrent Neural Networks

Year: 2017

Type: conference-paper

Abstract: We introduce in this work an efficient approach for audio scene classification using deep recurrent neural networks. An audio scene is firstly transformed into a sequence of high-level label tree embedding feature vectors. The vector sequence is then divided into multiple subsequences on which a deep GRU-based recurrent neural network is trained for sequence-to-label classification. The global predicted label for the entire sequence is finally obtained via aggregation of subsequence classification outputs. We will show that our approach obtains an F1-score of 97.7% on the LITIS Rouen dataset, which is the largest dataset publicly available for the task. Compared to the best previously reported result on the dataset, our approach is able to reduce the relative classification error by 35.3%.

Source: Interspeech (USB)

DOI: 10.21437/interspeech.2017-101

Authors: Huy Phan, Philipp Koch, Fabrice Katzberg, Marco Maass, Radoslaw Mazur, Alfred Mertins

Institution: University of Lübeck

Language: English

Topic: Music and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Deutsche Forschungsgemeinschaft

Awards: G2311450084, G6416031575