FET SMALL SIGNAL MODELLING BASED ON THE DST AND MEL FREQUENCY CEPSTRAL COEFFICIENTS

Fet Small-signal Modelling Based on the Dst and Mel Frequency Cepstral Coefficients

Abstract—In this paper, a new technique is proposed for field effect transistor (FET) small-signal modeling using neural networks. This technique is based on the combination of the Mel frequency cepstral coefficients (MFCCs) and discrete sine transform (DST) of the inputs to the neural networks. The input data sets to traditional neural systems for FET small-signal modeling are the scattering p...

Speech production based on the mel-frequency cepstral coefficients

Mel Frequency Cepstral Coefficients for Music Modeling

We examine in some detail Mel Frequency Cepstral Coefficients (MFCCs) the dominant features used for speech recognition and investigate their applicability to modeling music. In particular, we examine two of the main assumptions of the process of forming MFCCs: the use of the Mel frequency scale to model the spectra; and the use of the Discrete Cosine Transform (DCT) to decorrelate the Mel-spec...

Computing Mel-frequency cepstral coefficients on the power spectrum

In this paper we present a method to derive Mel-frequency cepstral coefficients directly from the power spectrum of a speech signal. We show that omitting the filterbank in signal analysis does not affect the word error rate. The presented approach simplifies the speech recognizer’s front end by merging subsequent signal analysis steps into a single one. It avoids possible interpolation and dis...

Speech reconstruction from mel frequency cepstral coefficients and pitch frequency

This paper presents a novel low complexity, frequency domain algorithm for reconstruction of speech from the melfrequency cepstral coe cients (MFCC), commonly used by speech recognition systems, and the pitch frequency values. The reconstruction technique is based on the sinusoidal speech representation. A set of sine-wave frequencies is derived using the pitch frequency and voicing decisions, ...