  Denoising is the process of removing noise from sound signals while improving
the quality and adequacy of the sound signals. Denoising sound has many
applications in speech processing, sound events classification, and machine
failure detection systems. This paper describes a method for creating an
autoencoder to map noisy machine sounds to clean sounds for denoising purposes.
There are several types of noise in sounds, for example, environmental noise
and generated frequency-dependent noise from signal processing methods. Noise
generated by environmental activities is environmental noise. In the factory,
environmental noise can be created by vehicles, drilling, people working or
talking in the survey area, wind, and flowing water. Those noises appear as
spikes in the sound record. In the scope of this paper, we demonstrate the
removal of generated noise with Gaussian distribution and the environmental
noise with a specific example of the water sink faucet noise from the induction
motor sounds. The proposed method was trained and verified on 49 normal
function sounds and 197 horizontal misalignment fault sounds from the Machinery
Fault Database (MAFAULDA). The mean square error (MSE) was used as the
assessment criteria to evaluate the similarity between denoised sounds using
the proposed autoencoder and the original sounds in the test set. The MSE is
below or equal to 0.14 when denoise both types of noises on 15 testing sounds
of the normal function category. The MSE is below or equal to 0.15 when
denoising 60 testing sounds on the horizontal misalignment fault category. The
low MSE shows that both the generated Gaussian noise and the environmental
noise were almost removed from the original sounds with the proposed trained
autoencoder.
