# Mouth Sounds: A Review of Acoustic Applications and Methodologies
## 4 Main Findings
### Mental Health
#### 4.4.3 Other Applications  
A. Sleep Disorders  
Breathing sounds can be an effective method of sleep monitoring and the early diagnosis of sleep disorders. In [133], a novel audio-based algorithm was proposed to estimate the four sleep stages and attend chronic sleep disorders. The breathing and snoring sounds of a male were recorded while asleep. Extracting the periodicity, intensity, variation, and non-linearity for each audio, a decision tree was developed using the vertical box control chart to detect the breath or snore sound. The results showed an accuracy of classifying deep and light sleep of 97.8%.  
In the same form, in [134], a snore-detection algorithm was developed by combining two NNs. Five microphones were placed around a bed to record the snoring of people. A combination of a convolutional NN and a recurrent NN using a constant-Q transformation spectrogram was employed. It was found that the performance of the proposed method was more related to the distance than the position of the microphones.  
Some studies focused their attention on one disorder. Obstructive sleep apnea is a pervasive disorder present in patients with breathing problems, being one of the most severe diseases related to the respiratory process [68; 113]. Using microphones from smartphones seemed to be the form that helped users feel less uncomfortable while sleeping. In [68], the average accuracy of apnea and hypopnea detection was 81.79%, while in [113], it reached 92% using a WB-microphone.  
B. Speech and Communication/ExpressionOver time, speech has worked as a communication tool between individuals. For completing the process of receiving and understanding auditory messages, researchers have looked for different situations where it is difficult to accomplish. In [55], a novel complex method solved the cocktail party problem while analyzing images and audio from recordings. The algorithm outperformed other speech separation methods when combining information from two speakers.  
The ability to produce sounds with the mouth has helped humans to express feelings through art. In [50], the acoustic and auditory short-effect of vocal warm-up in theatre actors was determined. Acoustic parameters, such as five formants F1-F5, jitter, duration, shimmer, NHR, and dynamic range, did not show noticeable differences. Still, the self-confidence of the actors improved their performance after the warm-up.  
Furthermore, using applications on smart devices to encourage and record vocalization demonstrated an improvement in children. The system analyzed recordings in terms of pitch and loudness [10]. The results indicated a strong correlation between the app's performance and assessments by human experts.  
Finally, in [118], the melody of human whistling was extracted using time-frequency analysis. Their method consisted of four stages: (1) audio recording transformation into the frequency domain, (2) single-frequency value extraction, (3) conversion to a note number corresponding to a MIDI standard, and (4) MIDI file creation with identified notes. The algorithm showed promising results, with an average of 92.7% accuracy for detecting musical errors.