Emotional voice conversion using neural networks with arbitrary scales F0 based on wavelet transform
نویسندگان
چکیده
منابع مشابه
Emotional voice conversion using neural networks with arbitrary scales F0 based on wavelet transform
An artificial neural network is an important model for training features of voice conversion (VC) tasks. Typically, neural networks (NNs) are very effective in processing nonlinear features, such as Mel Cepstral Coefficients (MCC), which represent the spectrum features. However, a simple representation of fundamental frequency (F0) is not enough for NNs to deal with emotional voice VC. This is ...
متن کاملEmotional Voice Conversion Using Neural Networks with Different Temporal Scales of F0 based on Wavelet Transform
An artificial neural network is one of the most important models for training features of voice conversion (VC) tasks. Typically, neural networks (NNs) are very effective in processing nonlinear features, such as mel cepstral coefficients (MCC) which represent the spectrum features. However, a simple representation for fundamental frequency (F0) is not enough for neural networks to deal with an...
متن کاملEmotional Voice Conversion with Adaptive Scales F0 Based on Wavelet Transform Using Limited Amount of Emotional Data
Deep learning techniques have been successfully applied to speech processing. Typically, neural networks (NNs) are very effective in processing nonlinear features, such as mel cepstral coefficients (MCC), which represent the spectrum features in voice conversion (VC) tasks. Despite these successes, the approach is restricted to problems with moderate dimension and sufficient data. Thus, in emot...
متن کاملContinuous wavelet transform with arbitrary scales and O(N) complexity
The continuous wavelet transform (CWT) is a common signal-processing tool for the analysis of nonstationary signals. We propose here a new B-spline-based method that allows the CWT computation at any scale. A nice property of the algorithm is that the computational cost is independent of the scale value. Its complexity is of the same order as that of the fastest published methods, without being...
متن کاملVoice Conversion using Convolutional Neural Networks
The human auditory system is able to distinguish the vocal source of thousands of speakers, yet not much is known about what features the auditory system uses to do this. Fourier Transforms are capable of capturing the pitch and harmonic structure of the speaker but this alone proves insufficient at identifying speakers uniquely. The remaining structure, often referred to as timbre, is critical...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: EURASIP Journal on Audio, Speech, and Music Processing
سال: 2017
ISSN: 1687-4722
DOI: 10.1186/s13636-017-0116-2