快速傅立叶变换、滑动窗口和某些Linux音频编程可以为您提供符合您所选语言的音调识别能力。
Fast Fourier transforms, sliding percentage Windows, and some Linux audio programming can give you tonal-recognition capabilities with your language of choice.
主要工作有：基于快速傅立叶变换与声学模型的音频数字水印算法研究。
Main ideas as follows: Research of audio digital watermark Algorithm based on Fast Fourier Transforms and psychoacoustic auditory model.
基于倒谱变换的音频盲水印算法。
The blind audio watermarking algorithm based on cepstrum transform.
提出一种基于倒谱变换的数字音频健壮盲水印算法。
This paper proposes a robust and blind digital audio watermarking algorithm based on cepstrum transform.
针对音频数据库中存在的问题，提出了一种基于索引的变换。
A transformation based on index was developed for the audio database.
提出了量化mclt变换系数的音频数字水印盲检新算法。
A new audio digital watermarking blind algorithm based on MCLT coefficient quantization is presented.
介绍了采用D类放大器来完成音频信号变换与放大的电路设计。
This paper introduces design of the circuit which uses a class-D amplifier to converse and enlarge the audio signal.
音频处理的数字化是利用数字滤波算法对采集到的音频信号进行变换处理来实现的。
The audio processing digitization is using the digital filter algorithm to sample and process the audio signal.
通过对小波变换表征信号突变原理的研究，给出了基于小波变换的音频信号基频提取的原理和算法。
By researching on the theory of the signal sudden change token by wavelet, this paper provides the principle and algorithm of audio signal pitch extraction based on wavelet principle.
针对音频数据库中存在的问题 ，提出了一种基于索引的变换 。
This article mainly introduces the methods of handling audio data in teaching components on web.
经处理得到的输出音乐节奏和音频能量作为控制信号调节音乐喷泉的花型变换节奏以及水柱高度。
The rhythms output of the proposed system can be used as control signal for music fountain and lightening for synchronization.
提出了一种基于小波分解和倒谱技术的音频数字水印算法，该算法通过对原始音频进行小波多级分解，从中选取低频系数进行倒谱变换。
Based on wavelet decomposition and cepstrum technology, an audio watermarking algorithm was proposed in which the low-frequency wavelet coefficients were chosen for cepstrum transforming.
提出了一种基于小波分解和倒谱技术的音频数字水印算法，该算法通过对原始音频进行小波多级分解，从中选取低频系数进行倒谱变换。
Based on wavelet decomposition and cepstrum technology, an audio watermarking algorithm was proposed in which the low-frequency wavelet coefficients were chosen for cepstrum transforming.
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