Condition monitoring of machines working under non-stationary operations is one of the most challenging problems in maintenance. A wind turbine is an example of such class of machines. One of effective approaches may be to identify operating conditions and investigate their influence on used diagnostic features. Commonly used methods based on measurement of electric current, rotational speed, power and other process variables require additional equipment (sensors, acquisition cards) and software. It is proposed to use advanced signal processing techniques for instantaneous shaft speed recovery from a vibration signal. It may be used instead of extra channels or in parallel as signal verification.

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common and high-risk sleep-related breathing disorder. Snoring detection is a simple and non-invasive method. In many studies, the feature maps are obtained by applying a short-time Fourier transform (STFT) and feeding the model with single-channel input tensors. However, this approach may limit the potential of convolutional networks to learn diverse representations of snore signals. This paper proposes a snoring sound detection algorithm using a multi-channel spectrogram and convolutional neural network (CNN). The sleep recordings from 30 subjects at the hospital were collected, and four different feature maps were extracted from them as model input, including spectrogram, Mel-spectrogram, continuous wavelet transform (CWT), and multi-channel spectrogram composed of the three single-channel maps. Three methods of data set partitioning are used to evaluate the performance of feature maps. The proposed feature maps were compared through the training set and test set of independent subjects by using a CNN model. The results show that the accuracy of the multi-channel spectrogram reaches 94.18%, surpassing that of the Mel-spectrogram that exhibits the best performance among the single-channel spectrograms. This study optimizes the system in the feature extraction stage to adapt to the superior feature learning capability of the deep learning model, providing a more effective feature map for snoring detection.

The issue of auditory segregation of simultaneous sound sources has been addressed in speech research but was given less attention in musical acoustics. In perception of concurrent speech, or speech with noise, the operation of time-frequency masking was often used as a research tool. In this work, an ex- tension of time-frequency masking, leading to the removal of spectro-temporal overlap between sound sources, was applied to musical instruments playing together. The perception of the original mixture was compared with the perception of the same mixture with all spectral overlap electronically removed. Ex- periments differed in the method of listening (headphones or a loudspeaker), sets of instruments mixed, and populations of participants. The main findings were: (i) in one of the experimental conditions the removal of spectro-temporal overlap was imperceptible, (ii) perception of the effect increased when removal of spectro-temporal overlap was performed in larger time-frequency regions rather than in small ones, (iii) perception of the effect decreased in loudspeaker listening. The results support both the multiple looks hypothesis and the “glimpsing” hypothesis known from speech perception.