For sound event detection under various background noises at low SNR

this paper proposes a method that mixes the background noises with sound events into noisy samples to train classifiers. In the pre-processing stage

we use a voting method based on 2th to 6th intrinsic mode functions (IMFs) that generated from empirical mode decomposition (EMD)

to detect the endpoint of sound events and estimate the SNR. Then subband power distribution (SPD) is used to extract features from audio data. Finally

we mix the background noise and all the sound event samples in the sound event database according to the estimated SNR

and then extract the noisy samples features to train multi-random forests (M-RF) for the detection of the sound events in low SNR environment. The experiment proves that the proposed method has the ability to recognize sound events in various acoustic scenes at low SNR

and can remain an average accuracy rate of 67.1% at-5dB.