歌唱语言正音在艺术专业教学中的实践论叙

歌唱语言正音课是高校艺术专业中的必修课程,在提升学生的歌唱语言水平的同时,也提高了学生的专业素养,更是学生专业学习的系统化过程。语言的矫正对于学习歌唱的人来说具有重要意义,歌唱语言的标准与否直接关系到音乐作品的呈现效果,以及是否充分发挥音乐作品的艺术感染力。本文将从课程建设和教学思考等方面展开论述。

对于高校声乐课堂教学改革的思考

自1977年恢复高考以来,我国声乐人才的培养渐渐地由各个地方高等院校聘请国内外知名的歌唱家,声乐教育家担负起来,成立了专业的音乐院系,吸纳社会各界的歌唱人才。声乐教学经历了土洋审美之争,教法的百家争鸣,开始随着社会大众审美的需求,在百变中趋同。有了一套相对稳定的教学大纲和教学模式。但是,随着高校扩招生源质量下降,社会音乐市场对于歌唱人才的多样化需求,音乐专业毕业生就业难等问题的出现,我们的声乐教学应当时刻关注人才培养模式的具体执行,在趋同中不断调整教学方式,适应社会对于人才的需求,提高人才培养的效率。

A fuzzy adaptive smoothing approach to robust endpoint detection based on MDL using sub-band speech

To develop a more robust endpoint detection algorithm, this paper first proposes a fuzzy adaptive smoothing algorithm. The general idea underlying adaptive smoothing is to adapt the short-term sub-band mean of the amplitude to the local attributes of speech on the basis of discontinuity measures. The adaptive smoothing algorithm in this paper utilizes a scale-space framework through the minimal description length （MDL）. We recommend using the fuzzy muhi-attribute decision making approach to select the proper sub-bands where the word boundary can be more reliably detected. The process and simulation of the fuzzy adaptive smoothing algorithm are given. The parameters utilize the mean amplitude of the audible frequency range （300 -3 700 Hz） and the sub-band mean of the amplitude （16 band filter-bank）. We selected the audible band energy because of its usefulness in detecting high-energy regions and making the distinction between speech and noise. Otherwise, the fuzzy adaptive smoothing algorithm is processed in sub-band speech to utilize the full range of frequency information.