Environment Sound Classification Based on Visual Multi-Feature Fusion and GRU-AWS

There are two major questions regarding Environmental Sound Classification (ESC). What is the best audio recognition framework, and what is the most robust audio feature? For investigating above problems, the Gated Recurrent Unit (GRU) network was used to analyze the effect of single features such a...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.191100-191114
Main Authors: Peng, Ning, Chen, Aibin, Zhou, Guoxiong, Chen, Wenjie, Zhang, Wenzhuo, Liu, Jing, Ding, Fubo
Format: Article
Language:English
Subjects:
Background noise
Classification
ESC
Experiments
Feature extraction
GRU
GRU-AWS
Hidden Markov models
LM-MFCC
Mel frequency cepstral coefficient
multi-feature fusion
Neural networks
sequence transformation
Spectrogram
Task analysis
Time-domain analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:There are two major questions regarding Environmental Sound Classification (ESC). What is the best audio recognition framework, and what is the most robust audio feature? For investigating above problems, the Gated Recurrent Unit (GRU) network was used to analyze the effect of single features such as Mel Scale Spectrogram (Mel), Log-Mel Scale Spectrogram (LM), and Mel frequency cepstral coefficient (MFCC) as well as multi-feature about Mel-MFCC, LM-MFCC, and Mel-LM-MFCC (T-M) in this paper. The experiment results show that in the ESC tasks, multi-features are better than the single features in the same dimensions, and LM-MFCC has the strongest robustness. Meanwhile, reverse sequence MFCC (R-MFCC) and forward and reverse mixed sequence MFCC (FR-MFCC) were also proposed to study the effects of sequence changes on audio. The experiment results show that the sequence transformation of audio features has less influence on the recognition tasks. Furthermore, to investigate the ESC task further we introduced the attention weight similar model (AWS) in to the multi-feature. The AWS model allows different audio feature attention weights of the same sound to learn from each other. It makes the GRU-AWS model focus on the frame-level features more effectively. The experiment results show that the GRU-AWS gets excellent performance with a recognition rate of 94.3%, and it outperforms other state-of-the-art methods.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3032226