Abstract
Machine listening plays an important role in machine-human interaction applications recent years. The prospect of making the computer to imitate the learning ability of human brain also became a popular issue with the rise of neural networks. Imagine that we go to a new place where labeled sound data is not available. How to let the users know what sound events happen frequently in a period of time by applying machine learning methods? These kinds of unsupervised learning applications are relatively rare in other machine listening research. We proposed this idea and also try to use neural networks and other unsupervised algorithms to summarize sound events that happen repeatedly in a place. In the simulation experiments of our thesis, we take self-recorded audio including common indoor sounds such as people talking and object collision sounds. Two electrical alarm sounds are also designed as target sound events, which the duration of each event is less than 10% of the total recording time. Frist, we take the sound signal and apply Fourier transform, then pass through the Mel-frequency filter bank to obtain Mel-spectrogram as our feature. Restricted Boltzmann machine of neural networks is chosen as our training model. Finally, we use clustering algorithm and successfully summarize the spectrogram that happens repeatedly. The user can distinguish the two target sound events through listen to the summarized sound events.