A frequency-rich carrier signal, such as a saw wave or white noise, is needed for the filters to properly convey the speech data. The amplitude of each of these bands is recorded and subsequently decoded when the process is reversed by applying those amplitudes to bandpass filters through which another signal, called the carrier, is run. A standard vocoder works by taking the voice signal, which is called the modulator, and splitting it into a number of frequency bands the more bands, the higher the quality.
The vocoder, or "voice encoder," was developed in the late 1930s by Bell Labs in Murray Hill, New Jersey as a way to code speech in order to reduce its bandwidth for transmission over long distances. Let me give you a little background on the vocoder.
The only sound source is my voice, which is processed through a vocoder created in Max/MSP and will continue to be altered in different ways throughout the rest of this piece. Hello, my name is Jack Callahan and I perform as "die Reihe." The piece on this record is organized into seven discreet sections, each separated by twelve seconds of silence. The idea behind the Sliding Discrete Fourier Transform (SDFT) is to make use of the known values of 2 to calculate the value for the next window.Section I. In the traditional implementation the window is moved on / by samples, usually /10+ with, and the DFT is recalculated. operations to calculate for a window of size. uses a divide-and-conquer method and costs +*.
Finally we assess the quality of transformations based on the SDFT in a Csound implementation. We also propose a much more efficient Simple Sliding Inverse DFT that makes sliding a serious alternative to jumping between overlapping frames. In this paper we review the mathematical background, and implementation issues, and then consider the advantages and disadvantages of the Sliding Discrete Fourier Transform (SDFT) as compared with a more traditional FFT algorithm. The existence of the sliding DFT has been known for some time, but it does not seem to be in wide use, possibly because of its perceived computational cost. The main problems for pitch shifting with envelope preservation in a phase vocoder are identified and a simple yet efficient remedy is proposed.
The application of the true envelope estimator in a pitch shifting application is investigated. The resulting implementation of the algorithm has slightly increased computational complexity compared to the standard LPC algorithm but offers significantly improved control over the envelope characteristics. As a remedy for the ringing effects in the the spectral envelope that are due to the rectangular filter used for spectral smoothing we propose the use of a Hamming window as smoothing filter. By means of controlled sub-sampling of the log amplitude spectrum and by means of a simple step size control for the iterative algorithm the run time of the algorithm can be decreased by a factor of 2.5-11. As the most promising algorithm the cepstrum based iterative true envelope estimator is selected. As a first step the different existing envelope estimation algorithms are investigated and their specific properties discussed. The intended application for the developed algorithm is pitch shifting with preservation of the spectral envelope in the phase vocoder. In this article the estimation of the spectral envelope of sound signals is addressed.