In music teaching in high schools, teachers should not only impart corresponding music knowledge to students but also focus on cultivating students’ comprehensive quality and quality. In the process of music teaching in universities, we should focus on students’ psychological adjustment and emotional health care. In order to quantify the application effect of big data technology in music teaching reform, this paper takes piano teaching in universities as an example to conduct research and mainly analyzes the physiological and psychological prediction effects involved in the teaching process. This paper introduces three typical big data technologies to predict and analyze the data involved in the existing piano teaching in universities. The analysis results show that the prediction effect based on the fuzzy neural inference system is the best. In addition, the three-dimensional display of the prediction data shows that the prediction effect obtained by big data technology shows good consistency and continuity, which indicates that the prediction method based on big data technology is suitable for psychological adjustment and emotional health care in music teaching in universities.

Electronic music is susceptible to noise in production, which needs to be processed. This paper analyzed several commonly used noise reduction algorithms, including wiener filtering, wavelet transform, spectral subtraction, and improved spectral subtraction, and then compared the noise reduction performance of several algorithms by producing noisy music datasets in the audio analysis tool librosa. It was found from the experimental results that the wavelet transform algorithm performed best when sym3 was used as the wavelet basis function, and the number of decomposition layers was 7. The comparison of different algorithms showed that the performance of the wiener filtering algorithm was poor in reducing noise, and the signal-to-noise ratio (SNR) and signal distortion ratio (SDR) was low; the performance of the improved spectral subtraction algorithm was the best, and the SNR and SDR were 20.36 dB and 17.94 dB, respectively, when the SNR was −8 dB, which were better than the other algorithms. The experimental results demonstrate the reliability of the improved spectral subtraction method in music signal noise reduction. The algorithm can be applied in practical music processing.

In view of the underground moving target localization question, a solution has been proposed which is based on the integration of RFID and GIS. This paper presents relevant concepts of the RFID and GIS; analyzes the working principle, RFID reader positioning error and its installation methods as well, which are on the basis of the RFID and GIS moving target positioning system. In the paper, each of the moving targets carries an RFID tag which has the property information of the moving target. The distances from every RFID readers to their laneway entry have been stored in the database in order that the underground reader calculates and locates the position of moving target. Therefore, the system can realize the localization and track on underground moving targets, and thus can increase aid effectiveness by locating the affected staffs quickly and accurately. Moreover, the system is convenient for maintenance and ease to manage.

This paper is a study on the application of lexical-chunk theory proposed by Lewis in college English teaching, aiming at investigating what specific difficulties learners probably meet in chunk learning process by means of translation tests. Research results indicate that in the chunk learning process, students have indeed encountered various difficulties, such as syntactical functions, culture comprehension, preposition usage and verb-noun chunk collocation.