Audio Feature Engineering for Occupancy and Activity Estimation in Smart Buildings

The occupancy and activity estimation are fields that have been severally researched in the past few years. However, the different techniques used include a mixture of atmospheric features such as humidity and temperature, many devices such as cameras and audio sensors, or they are limited to speech recognition. In this work is proposed that the occupancy and activity can be estimated only from the audio information using an automatic approach of audio feature engineering to extract, analyze and select descriptors/variables. This scheme of extraction of audio descriptors is used to determine the occupation and activity in specific smart environments, such that our approach can differentiate between academic, administrative or commercial environments. Our approach from the audio feature engineering is compared to previous similar works on occupancy estimation and/or activity estimation in smart buildings (most of them including other features, such as atmospherics and visuals). In general, the results obtained are very encouraging compared to previous studies.

Music Teaching Reform in Colleges and Universities under the Background of the Development of Contemporary Music Discipline

Objectives: With the continuous progress of information technology, multimedia teaching forms with a large number of emerging educational technology development carry more audio-visual information, with rich pictures, demonstration images, audio-visual integration characteristics, which has been widely used in the process of music teaching in Colleges and universities. Methods: And it has gradually become the main teaching mode of music teaching in schools, and have been increasingly popular. Therefore, the development of modern music discipline and the reform of music teaching in schools are analyzed to find a suitable way for music teaching in schools, so as to gradually improve the development speed of music education in China. Results: The study based on digital audio related technology contained in CAT-based Solfeggio and ear training system provides an example to demonstrate the message mechanism. Audio feature extraction and matching technology are also discussed. Several types of audio feature extraction methods and their features are analyzed. Conclusion: The design of the whole framework and the realization of the core algorithm of Solfeggio and ear training learning assistant system are completed. Tests show that the algorithm can be well applied in the system, while making the system more flexible and scalable, and can be further extended.