1/7/2023 0 Comments Praat softwareSpectral minima in each frequency band without any distinction between In contrast to other methods, ourĪpproach does not use a voice activity detector. The method canīe combined with any speech enhancement algorithm which requires a noise Nonstationary noise when a noisy speech signal is given. We describe a method to estimate the power spectral density of Finally, we show anĪpplication of the RASTA technique to speech enhancement Models that are required and the relationship of these features to deltaįeatures and to cepstral mean subtraction. Relationship between RASTA features and the nature of the recognition Human auditory perception, and extend the original method toĬombinations of additive noise and convolutional noise. In this paper, we review the theoretical andĮxperimental foundations of the method, discuss the relationship with We have beenĮxperimenting with filtering approaches that attempt to exploit theseĭifferences to produce robust representations for speech recognition andĮnhancement and have called this class of representations relative The temporal properties of these environmental effects are quiteĭifferent from the temporal properties of speech. Impulse response and the addition of some environmental noise. Transformations and, in particular, by convolution with an environmental In someĬases, the environmental effect can be modeled by a set of simple Severely degrades in an unexpected communications environment. Performance of even the best current stochastic recognizers This method is then applied to synthetic signals and filtered speech signals. The two steps together provide a unique AM-FM or minimum- phase/all-phase decomposition of a signal. In the second step, the derivative of the error signal, which is the PIF, is computed. The resulting residual error signal is an all-phase or phase-only analytic signal. This method is called linear prediction in spectral domain (LPSD). The criterion that is optimized is a waveform flatness measure as opposed to the spectral flatness measure used in spectral analysis. In the first step, the envelope of the signal is approximated to desired accuracy using a minimum-phase approximation by using the dual of the autocorrelation method of linear prediction, well known in spectral analysis. This algorithm is new and does not have a counterpart in the cepstral literature. An algorithm is proposed for decomposing an analytic signal into two analytic signals, one completely characterized by its envelope and the other having a positive IF. This result paves the way for representing signals by positive envelopes and positive IF (PIF). In the special case of an analytic signal having poles and zeros in reciprocal complex conjugate locations about the unit circle in the complex-time plane, it is shown that their instantaneous frequency (IF) is always positive. Using this signal model, expressions are derived for the envelope, phase, and the instantaneous frequency of the signal s(t). Except, in this case, the poles and zeros are located in the complex-time plane. This type of representation is analogous to that used in discrete-time systems theory, where the periodic frequency response of a system is characterized by a finite number of poles and zeros in the z-plane. Moreover, the accuracy of the LSTM network using the deep learning algorithm in the objective evaluation of the broadcast timbre is better than the traditional HMM and GMM-UBM, and the proposed method can achieve about 95% accuracy rate in our database.Īn analytic signal s(t) is modeled over a T second duration by a pole- zero model by considering its periodic extensions. The experiments show that the selection of timbre features is scientific and effective. Finally, the three models of hidden Markov model (HMM), Gaussian Mixture Model-General Background Model (GMM-UBM), and long short-term memory (LSTM) are exploited to evaluate the timbre of the broadcast by extracting timbre features and four timbre feature combinations. Then, the timbre feature selection strategy is presented based on human vocal mechanism, and the broadcast timbre characteristics are divided into three categories, which include source parameters, vocal tract parameters, and human hearing parameters. Firstly, the broadcasting vocal timbre database is constructed on Chinese phonetic characteristics. In this paper, an objective evaluation method of broadcasting vocal timbre is proposed. The subjective evaluation method is widely used, but the selection results have certain subjectivity and uncertainty. In the selection process of broadcasting and hosting professionals, the vocal timbre is an important index. Broadcasting voice is used to convey ideas and emotions.
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