STFT-based speech enhancement by reconstructing the harmonics
نویسندگان
چکیده
A novel Short Time Fourier Transform (STFT) based speech enhancement method is introduced. This method enhances the magnitude spectrum of a noisy speech segment. The new idea that is used in this method is to basically reconstruct the harmonics at the multiples of the fundamental frequency ( 0 F ) rather than trying to improve them. The harmonics are produced, in the magnitude spectrum, using the knowledge of the window function we are using for the STFT. These harmonics are then scaled and laid on multiples of 0 F . Experimental results prove the effectiveness of this enhancement method in various noisy conditions and various SNR ratios.
منابع مشابه
Least squares estimate of the initial phases in STFT based speech enhancement
In this paper, we consider single-channel speech enhancement in the short time Fourier transform (STFT) domain. We suggest to improve an STFT phase estimate by estimating the initial phases. The method is based on the harmonic model and a model for the phase evolution over time. The initial phases are estimated by setting up a least squares problem between the noisy phase and the model for phas...
متن کاملSTFT Phase Improvement for Single Channel Speech Enhancement
In state-of-the-art single channel short-time Fourier transform (STFT) based speech enhancement algorithms only the amplitude of the noisy speech signal is improved, but its phase is left unchanged. It is commonly assumed that the noisy phase is the best estimate of the clean phase available. While using the noisy phase is indeed optimal under certain statistical assumptions, in this paper we s...
متن کاملEnhancement and Recognition of Reverberant and Noisy Speech by Extending Its Coherence
Most speech enhancement algorithms make use of the short-time Fourier transform (STFT), which is a simple and flexible time-frequency decomposition that estimates the short-time spectrum of a signal. However, the duration of short STFT frames are inherently limited by the nonstationarity of speech signals. The main contribution of this paper is a demonstration of speech enhancement and automati...
متن کاملSpeech spectral modeling and enhancement based on autoregressive conditional heteroscedasticity models
In this paper, we develop and evaluate speech enhancement algorithms, which are based on supergaussian generalized autoregressive conditional heteroscedasticity (GARCH) models in the short-time Fourier transform (STFT) domain. We consider three different statistical models, two fidelity criteria, and two approaches for the estimation of the variances of the STFT coefficients. The statistical mo...
متن کاملBlock Nonnegative Matrix Factorization for Single Channel Source Separation
Nonnegative Matrix Factorization (NMF) [1, 2] has been widely used in audio research, e.g. automatic music transcription [3], musical source separation [4], and speech enhancement [5]. The key strategy for applying NMF to audio-related tasks is to find a lower rank representation of the Short Time Fourier Transformed (STFT) input signal and use the basis vectors as dictionaries. For example, in...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2009