Classification of the fundamental frequency identification methods
DOI:
https://doi.org/10.18372/2310-5461.33.11553Keywords:
, identification audio signal, the pitch frequency, the period of the fundamental tone, the overtone spectrum of the audio signal, frequency range, time domainAbstract
A significant role in the formation of the voice characteristics of the information signals given to the speaker system represents a language group, which is responsible for basic voice parameters such as pitch frequency of the signal, information frames and properties frequency range, volume, frequency and rate of speech, intonation, etc. The main criteria features of voice were identified. The basic characteristics of audio signals generated by a person of a certain language group were analyzed. The methods and models to identify the fundamental frequency, which further gave the opportunity to determine the period of the fundamental tone were analyzed. In the current research, the determination of the fundamental frequency, there are problems such as the complexity of the implementation methods, and low probability of error in determination process; low resistance methods to external interference. Methods of determination of fundamental frequency with the plane of their implementation were classified.
References
Аль-Келані Ф. Дослідження характеристик мовного сигналу в задачах розпізнавання, 2004.
Ашихмін А. В. Підвищення точності та швидкості обчислення миттєвого спектра гармонійних сигналів за допомогою детектора основного тону, 2008.
A. M. Noll. Pitch determination of human speech by the harmonic product spectrum, the harmonic sum spectrum, and maximum likelihood estimate Proceedings of the Symposium on Computer Processing in Communications, April, 1969.
Widrow B., Stearns S. D., Burgess J. C. Adaptive signal processing edited by bernard widrow and samuel d. stearns // The Journal of the Acoustical Society of America, 1986. — Т. 80. — № 3. — С. 991–992.
Moorer J. A. 1973. The optimum comb method of pitch period analysis of continuous digitized speech AIM-207. Stanford: Stanford Artificial Intelligence Laboratory.
Medan Y., Yair E., Chazan D. Super resolution pitch determination of speech signals //IEEE transactions on signal processing, 1991. — Т. 39.— № 1. — С. 40–48.
Licklider J. C. R. A duplex theory of pitch perception // The Journal of the Acoustical Society of America, 1951. — Т. 23. — № 1. — С. 147–147.