Acoustic Event Detection in Multichannel Audio Using Gated Recurrent Neural Networks with High‐Resolution Spectral Features. Issue 6 (5th December 2017)

• Record Type: Journal Article
• Authors: Kim, Hyoung‐Gook; Kim, Jin Young
• Is Part Of: ETRI journal. Volume 39:Issue 6(2017)
• Page Start: 832
• Page End: 840
• Publication Date: 2017-12-05
• Subjects: Acoustic event detection -- Deep recurrent neural networks -- Gated recurrent neural network -- Multichannel audio; Telecommunication -- Periodicals; Electronics -- Periodicals; Electronics; Telecommunication; Periodicals; Periodicals; 621.38205
• DOI: 10.4218/etrij.17.0117.0157
• Languages: English
• ISSNs: 1225-6463

Abstract

Abstract : Recently, deep recurrent neural networks have achieved great success in various machine learning tasks, and have also been applied for sound event detection. The detection of temporally overlapping sound events in realistic environments is much more challenging than in monophonic detection problems. In this paper, we present an approach to improve the accuracy of polyphonic sound event detection in multichannel audio based on gated recurrent neural networks in combination with auditory spectral features. In the proposed method, human hearing perception‐based spatial and spectral‐domain noise‐reduced harmonic features are extracted from multichannel audio and used as high‐resolution spectral inputs to train gated recurrent neural networks. This provides a fast and stable convergence rate compared to long short‐term memory recurrent neural networks. Our evaluation reveals that the proposed method outperforms the conventional approaches.