In this paper, we propose an attention sinusoidal representation network (A-SIREN). Firstly, Mel -frequency cepstral coefficient (MFCC) as an audio recognition feature is extracted from a dataset. Then, feature extraction is performed on each frame of the MFCC by using a neural network named gated recurrent unit (GRU). And audio score is calculated for each frame by using sine function and the audio is re-weighted according to the audio score of each frame. Finally, the categories of environmental sound are discriminated by using the full connection layer in combination with the Softmax classifier. In the experiments of this paper, we validated the designed model in an open-source dataset Urban Sound 8K and compared the performance of the designed model with that of other models. Experimental results show that the A-SIREN works best on the Urban Sound 8K dataset with recognition rate as high as 93.5%.
PENG Ning, CHEN Aibin, ZHOU Guoxiong, CHEN Wenjie, LIU Jing
. Environmental Sound Recognition Based on Attention Sinusoidal Representation Network[J]. Journal of Applied Sciences, 2021
, 39(4)
: 641
-649
.
DOI: 10.3969/j.issn.0255-8297.2021.04.011
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