Speech recognition is an important field of digital signal processing. Automatic Speaker Recognition (ASR) objective is to extract features, characterize and recognize speaker. Mel Frequency Cepstral Coefficients (MFCC) is most widely used feature vector for ASR. MFCC is used for designing a text dependent speaker identification system. In this paper the DSP processor TMS320C6713 with Code Comp...

In this paper, we propose a deep learning-based method for classifying abnormal judgments of music song interpretation problems, using computer to record the singer’s voice and then analyze judge it with trained model, pointing out main problems that exist in singing process, which is practical significance self-learning related teaching guidance. We collected more than 300 singers’ audio, data...

Undoubtedly the compact representation by a set of Mel Frequency Cepstrum Coefficients (MFCC) has been used satisfactorily for ASR [9]. The cochlea is an organ, in humans or mammalians that converts the frequency perceived by the ear in punctual stimulation to excite the nerve auditory that receives a set of stimulus that comes from speech sound pressure. A new approach is proposed that conside...

A K-Nearest Neighbour Algorithm involving Mel-Frequency Cepstral Coefficients (MFCCs) is provided to perform Speech signal feature extraction for the task of speaker accent recognition. Mel-Frequency Cepstral Coefficient is effectively used to perform the feature extraction of the input signal. For each input signal the mean of the MFCC matrix is used for pattern recognition .The K-nearest neig...

The spectral parameters that result from filtering the frequency sequence of log mel-scaled filter-bank energies with a first or second order FIR filter have proved to be competitive for speech recognition. Recently, the authors have shown that this frequency filtering can approximately equalize the cepstrum variance enhancing the oscillations of the spectral envelope curve that are most effect...

Many authentication applications involving automatic speaker verification (ASV) demand robust performance using short-duration, fixed or prompted text utterances. Text constraints not only reduce the phone-mismatch between enrolment and test utterances, which generally leads to improved performance, but also provide an ancillary level of security. This can take the form of explicit utterance ve...

This paper examines the use of Mel-frequency Cepstral Coefficients in the classification of musical instruments. 2004 piano, violin and flute samples are analysed to get their coefficients. These coefficients are reduced using principal component analysis and used to train a multi-layered perceptron. The network is trained on the first 3, 4 and 5 principal components calculated from the envelop...

We examine the effect of listening level, i.e. the absolute sound pressure level at which sounds are reproduced, on music similarity, and in particular, on playlist generation. Current methods commonly use similarity metrics based on Mel-frequency cepstral coefficients (MFCCs), which are derived from the objective frequency spectrum of a sound. We follow this approach, but use the level-depende...