Classification of music genre has been an inspiring job in the area of music information retrieval (MIR). Classification of genre can be valuable to explain some actual interesting problems such as creating song references, finding related songs, finding societies who will like that specific song. The purpose of our research is to find best machine learning algorithm that predict the genre of s...

In this paper, we have analyzed the performance of speaker recognition system based on features extracted from the speech recorded using throat microphone in clean and noisy environment. In general, clean speech performs better for speaker recognition system. Speaker recognition in noisy environment, using transducer held at the throat results in a signal that is clean even in noisy. This speak...

Abstract Aiming at the issue that recognition accuracy of traditional acoustic signal features is low for helicopter signals with wind noise in near field, a method extracting mixed MFCC+GFCC based on wavelet decomposition proposed. Firstly, three-layer and reconstruction are applied to signals; then, Mel-Frequency Cepstral Coefficients (MFCC) Gammatone-Frequency Cepstrum Coefficient (GFCC) res...

The paper present effective method for recognition of digit, numbers. Most of speech recognition systems contain two main modules as follow “feature extraction” and “feature matching”. In this project, (MFCC) Mel Frequency Cepstrum coefficient algorithm is used to simulate feature extraction module. Using this algorithm, the Cepstral Coefficients are calculated on Mel frequency scale. VQ (vecto...

This paper examines and presents an approach to the recognition of speech signal using frequency spectral information with Mel frequency. It is a dominant feature for speech recognition. Mel-frequency cepstral coefficients (MFCCs) are the coefficients that collectively represent the shortterm power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a non linear m...

This paper presents the robust front-end algorithm that was submitted by Motorola to the ETSI STQ-Aurora DSR working group as a proposal for the Advanced DSR front-end in January 2001. The algorithm consists of a two-stage melwarped Wiener filter, a waveform processor, a channelnormalized mel-frequency cepstral calculation and a subsystem of post-cepstral processing according to the reliability...

In automatic speech recognition mel-frequency cepstral coefficients (MFCC) or linear predictive cepstral coefficients (LPCC) are features commonly used today. However, their calculation considers only a few features of the auditory system. On the assumption that the human representation of speech is an optimal representation, considering more features of the auditory system might lead to a bett...

In this paper, the use of new auditory-based features derived from cochlear filters, have been proposed for classification of unvoiced fricatives. Classification attempts have been made to classify sibilant (i.e., /s/, /sh/) vs. non-sibilants (i.e., /f/, /th/) as well as for fricatives within each sub-category (i.e., intra-sibilants and intra-non-sibilants). Our experimental results indicate th...

Nowadays synthetic voice is frequently used to defraud a biometric access system which are speaker recognition based. This paper presents synthetic speech detection in automatic speaker verification system (ASV) for the purpose of spoof detection. Feature extraction is done by canonical Mel Frequency Cepstral Coefficients (MFCC) algorithm and classification of natural and synthetic voice are do...