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An auditory-based feature extraction algorithm is presented. The feature is based on a recently published time-frequency transform plus a set of modules to simulate the signal processing functions in the cochlea. The feature is applied to a speaker identification task to address the acoustic mismatch problem between training and testing. Usually, the performances of acoustic models trained in clean...
Automatic musical instrument classification system deals with a large number of sound database and various types of features schemes. With the lack of data pre-processing, it might become invaluable asset that can impact the whole classification tasks. In handling an effective classification system, finding the best data sets with the best features schemes often a vital step in the data representation...
This paper tries to deal with the problem of performance degradation in emotion affected speech recognition. The F-ratio analysis method in statistics is utilized to analyze the significance of different frequency bands for speech unit classification. The result is then used to optimize filter bank design for Mel-frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) features...
The speech cepstral features are important parameter in automatic speech recognition (ASR), which symbolizes the property of human auditory system (HAS). The mel-frequency cepstral coefficients (MFCC) are the most widely used features in speech recognition field. This paper discusses about the algorithm of chirp Z-transform (CZT), and the CZT-based cepstral coefficients are proposed along with the...
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